From be206fc4c7f0b4d668e30228eff7901e5130f5f0 Mon Sep 17 00:00:00 2001
From: Teun Huijben <teun.huijben@czbiohub.org>
Date: Thu, 31 Jul 2025 00:02:30 -0400
Subject: [PATCH 01/15] added geff + added pyscipopt for ilpy to work on mac

---
 pixi.lock      | 1333 ++++++++++++++++++++++++------------------------
 pyproject.toml |    2 +
 2 files changed, 672 insertions(+), 663 deletions(-)

diff --git a/pixi.lock b/pixi.lock
index 56fcf5f..f56979a 100644
--- a/pixi.lock
+++ b/pixi.lock
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@@ -7311,6 +7308,7 @@ packages:
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 - pypi: https://files.pythonhosted.org/packages/e9/44/75a9c9421471a6c4805dbf2356f7c181a29c1879239abab1ea2cc8f38b40/sniffio-1.3.1-py3-none-any.whl
   name: sniffio
   version: 1.3.1
@@ -10701,10 +10707,10 @@ packages:
   - pkg:pypi/stack-data?source=hash-mapping
   size: 26988
   timestamp: 1733569565672
-- pypi: https://files.pythonhosted.org/packages/82/95/38ef0cd7fa11eaba6a99b3c4f5ac948d8bc6ff199aabd327a29cc000840c/starlette-0.47.1-py3-none-any.whl
+- pypi: https://files.pythonhosted.org/packages/f7/1f/b876b1f83aef204198a42dc101613fefccb32258e5428b5f9259677864b4/starlette-0.47.2-py3-none-any.whl
   name: starlette
-  version: 0.47.1
-  sha256: 5e11c9f5c7c3f24959edbf2dffdc01bba860228acf657129467d8a7468591527
+  version: 0.47.2
+  sha256: c5847e96134e5c5371ee9fac6fdf1a67336d5815e09eb2a01fdb57a351ef915b
   requires_dist:
   - anyio>=3.6.2,<5
   - typing-extensions>=4.10.0 ; python_full_version < '3.13'
@@ -10995,16 +11001,16 @@ packages:
   timestamp: 1735661472632
 - pypi: .
   name: trackedit
-  version: 0.0.3
-  sha256: 2a712ec094b7454462563b60f620a3718fc16f787668dba269b67ab87f64d8e5
+  version: 0.0.4
+  sha256: b0ded4b376edca4555d32c4ff787ab3905a342de022b10da1f3e89040e792230
   requires_dist:
   - mip>=1.16rc0
   requires_python: '>=3.10'
   editable: true
 - pypi: .
   name: trackedit
-  version: 0.0.3
-  sha256: 2a712ec094b7454462563b60f620a3718fc16f787668dba269b67ab87f64d8e5
+  version: 0.0.4
+  sha256: b0ded4b376edca4555d32c4ff787ab3905a342de022b10da1f3e89040e792230
   requires_dist:
   - mip>=1.16rc0
   requires_python: '>=3.10'
@@ -11048,7 +11054,7 @@ packages:
   license: MIT
   license_family: MIT
   purls:
-  - pkg:pypi/typer?source=compressed-mapping
+  - pkg:pypi/typer?source=hash-mapping
   size: 77232
   timestamp: 1748304246569
 - conda: https://conda.anaconda.org/conda-forge/noarch/typer-slim-0.16.0-pyhe01879c_0.conda
@@ -11066,7 +11072,7 @@ packages:
   license: MIT
   license_family: MIT
   purls:
-  - pkg:pypi/typer-slim?source=compressed-mapping
+  - pkg:pypi/typer-slim?source=hash-mapping
   size: 46798
   timestamp: 1748304246569
 - conda: https://conda.anaconda.org/conda-forge/noarch/typer-slim-standard-0.16.0-hf964461_0.conda
@@ -11100,7 +11106,7 @@ packages:
   license: MIT
   license_family: MIT
   purls:
-  - pkg:pypi/typing-inspection?source=compressed-mapping
+  - pkg:pypi/typing-inspection?source=hash-mapping
   size: 18809
   timestamp: 1747870776989
 - conda: https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.14.1-pyhe01879c_0.conda
@@ -11245,9 +11251,9 @@ packages:
   - watchfiles>=0.13 ; extra == 'standard'
   - websockets>=10.4 ; extra == 'standard'
   requires_python: '>=3.9'
-- conda: https://conda.anaconda.org/conda-forge/noarch/virtualenv-20.31.2-pyhd8ed1ab_0.conda
-  sha256: 763dc774200b2eebdf5437b112834c5455a1dd1c9b605340696950277ff36729
-  md5: c0600c1b374efa7a1ff444befee108ca
+- conda: https://conda.anaconda.org/conda-forge/noarch/virtualenv-20.32.0-pyhd8ed1ab_0.conda
+  sha256: 7a6eb58af8aa022202ca9f29aa6278f8718780a190de90280498ffd482f23e3e
+  md5: 3d6c6f6498c5fb6587dc03ff9541feeb
   depends:
   - distlib >=0.3.7,<1
   - filelock >=3.12.2,<4
@@ -11257,8 +11263,8 @@ packages:
   license_family: MIT
   purls:
   - pkg:pypi/virtualenv?source=hash-mapping
-  size: 4133755
-  timestamp: 1746781585998
+  size: 4135484
+  timestamp: 1753096346652
 - conda: https://conda.anaconda.org/conda-forge/linux-64/vispy-0.14.3-py311h8184624_1.conda
   sha256: 6e4867c607b37fb7112c83b568d3508b6b7b4483674629d3ddf77251f52f1d9b
   md5: 8133810d1118d66d3ac96f16d041e51b
@@ -11684,24 +11690,25 @@ packages:
   purls: []
   size: 86425
   timestamp: 1749230316106
-- conda: https://conda.anaconda.org/conda-forge/linux-64/yaml-0.2.5-h7f98852_2.tar.bz2
-  sha256: a4e34c710eeb26945bdbdaba82d3d74f60a78f54a874ec10d373811a5d217535
-  md5: 4cb3ad778ec2d5a7acbdf254eb1c42ae
+- conda: https://conda.anaconda.org/conda-forge/linux-64/yaml-0.2.5-h280c20c_3.conda
+  sha256: 6d9ea2f731e284e9316d95fa61869fe7bbba33df7929f82693c121022810f4ad
+  md5: a77f85f77be52ff59391544bfe73390a
   depends:
-  - libgcc-ng >=9.4.0
+  - libgcc >=14
+  - __glibc >=2.17,<3.0.a0
   license: MIT
-  license_family: MIT
   purls: []
-  size: 89141
-  timestamp: 1641346969816
-- conda: https://conda.anaconda.org/conda-forge/osx-arm64/yaml-0.2.5-h3422bc3_2.tar.bz2
-  sha256: 93181a04ba8cfecfdfb162fc958436d868cc37db504c58078eab4c1a3e57fbb7
-  md5: 4bb3f014845110883a3c5ee811fd84b4
+  size: 85189
+  timestamp: 1753484064210
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/yaml-0.2.5-h925e9cb_3.conda
+  sha256: b03433b13d89f5567e828ea9f1a7d5c5d697bf374c28a4168d71e9464f5dafac
+  md5: 78a0fe9e9c50d2c381e8ee47e3ea437d
+  depends:
+  - __osx >=11.0
   license: MIT
-  license_family: MIT
   purls: []
-  size: 88016
-  timestamp: 1641347076660
+  size: 83386
+  timestamp: 1753484079473
 - conda: https://conda.anaconda.org/conda-forge/linux-64/yarl-1.20.1-py311h2dc5d0c_0.conda
   sha256: 9b6cce2794e836a43679d733b8bdbafeed45ff534c338b84d439ed55cd7b2170
   md5: 18c288aa6aae90e2fd8d1cf01d655e4f
@@ -11869,12 +11876,12 @@ packages:
   purls: []
   size: 84057
   timestamp: 1679095058609
-- pypi: https://files.pythonhosted.org/packages/00/ed/d8c3f56c1edb0ee9b51461dd08580382e9589850f769b69f0dedccff5215/zope_event-5.1-py3-none-any.whl
+- pypi: https://files.pythonhosted.org/packages/e9/04/fd55695f6448abd22295fc68b2d3a135389558f0f49a24b0dffe019d0ecb/zope_event-5.1.1-py3-none-any.whl
   name: zope-event
-  version: '5.1'
-  sha256: 53de8f0e9f61dc0598141ac591f49b042b6d74784dab49971b9cc91d0f73a7df
+  version: 5.1.1
+  sha256: 8d5ea7b992c42ce73a6fa9c2ba99a004c52cd9f05d87f3220768ef0329b92df7
   requires_dist:
-  - setuptools
+  - setuptools>=75.8.2
   - sphinx ; extra == 'docs'
   - zope-testrunner ; extra == 'test'
   requires_python: '>=3.9'
diff --git a/pyproject.toml b/pyproject.toml
index 9551c38..a050471 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -67,6 +67,7 @@ preview = ["pixi-build"]
 
 [tool.pixi.pypi-dependencies]
 ultrack = { git = "https://github.com/royerlab/ultrack.git", rev = "18162481724917475d2ed503b432b5ed21307237" }
+geff = {git = "https://github.com/live-image-tracking-tools/geff.git"}
 
 [tool.pixi.feature.editable.pypi-dependencies]
 trackedit = { path = ".", editable = true }
@@ -89,6 +90,7 @@ pyqt = ">=5.15.9,<6"
 numpy = "<2.2"
 pre-commit = ">=4.1.0,<5"
 dask = ">=2025.2.0,<2026"
+pyscipopt = "*"  #for ilpy (on mac)
 
 [tool.pixi.feature.test.dependencies]
 pytest = "*"

From 37dd4272b27d510c5d9e1dbc4c10a6c0d7bb2af0 Mon Sep 17 00:00:00 2001
From: TeunHuijben <teun.huijben@czbiohub.org>
Date: Thu, 5 Feb 2026 14:37:26 -0800
Subject: [PATCH 02/15] get metadata from geff and save as metadata.toml when
 converting geff to db

---
 pixi.lock               |  4 ++--
 trackedit/utils/geff.py | 20 ++++++++++++++++++--
 2 files changed, 20 insertions(+), 4 deletions(-)

diff --git a/pixi.lock b/pixi.lock
index a942b64..da1f8b4 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -10440,7 +10440,7 @@ packages:
   requires_python: '>=3.7'
 - pypi: .
   name: trackedit
-  version: 0.0.10
+  version: 0.0.12
   sha256: 53d7ebc64418c7e2c6c3b9b83ee5341fa024c3fae629304a75d31deb78d8bff3
   requires_dist:
   - click>=8.0
@@ -10450,7 +10450,7 @@ packages:
   editable: true
 - pypi: .
   name: trackedit
-  version: 0.0.10
+  version: 0.0.12
   sha256: 53d7ebc64418c7e2c6c3b9b83ee5341fa024c3fae629304a75d31deb78d8bff3
   requires_dist:
   - click>=8.0
diff --git a/trackedit/utils/geff.py b/trackedit/utils/geff.py
index 311a6dc..c485964 100644
--- a/trackedit/utils/geff.py
+++ b/trackedit/utils/geff.py
@@ -197,8 +197,24 @@ def convert_geff_to_db(geff_path: Path, output_path: Path = None) -> None:
     print(f"✓ Inserted {len(edge_records)} edges")
 
     print(f"✓ Database saved to: {database_path}")
-    print("Don't forget to create a metadata.toml file with the following content: ")
-    print("shape = [ 5, 128, 128, 128] ([t, (z, ), y, x])")
+
+    # Extract and save shape metadata to TOML file
+    if geff_metadata.extra and "tracksdata" in geff_metadata.extra:
+        shape = geff_metadata.extra["tracksdata"].get("shape")
+        if shape:
+            metadata_path = database_path.parent / "metadata.toml"
+            shape_str = ", ".join(str(s) for s in shape)
+            toml_content = f"shape = [ {shape_str},]\n"
+            metadata_path.write_text(toml_content)
+            print(f"✓ Saved metadata to: {metadata_path}")
+        else:
+            print("No shape found in GEFF metadata extra/tracksdata")
+    else:
+        print("⚠ No extra/tracksdata metadata found in GEFF file")
+        print(
+            "Don't forget to create a metadata.toml file with the following content: "
+        )
+        print("shape = [ 5, 128, 128, 128] ([t, (z, ), y, x])")
 
 
 @click.command()

From a4e4642cbdbd1b0e8285b5c0de66bb58e57b34bd Mon Sep 17 00:00:00 2001
From: TeunHuijben <teun.huijben@czbiohub.org>
Date: Tue, 10 Feb 2026 15:49:32 -0800
Subject: [PATCH 03/15] fix 3D selection of labels by clicking

---
 trackedit/motile_overwrites.py | 66 ++++++++++++++++++++++++++++++++++
 1 file changed, 66 insertions(+)

diff --git a/trackedit/motile_overwrites.py b/trackedit/motile_overwrites.py
index 7f6d40b..5ee577c 100644
--- a/trackedit/motile_overwrites.py
+++ b/trackedit/motile_overwrites.py
@@ -284,6 +284,72 @@ def patched_create_pyqtgraph_content(self, track_df, feature):
 
 TreePlot._create_pyqtgraph_content = patched_create_pyqtgraph_content
 
+
+# Patch TrackLabels click handler to fix DatabaseArray lazy loading issue
+def patch_track_labels_click_handler():
+    """Monkey patch TrackLabels to add DatabaseArray loading workaround.
+
+    After colormap updates, napari's get_value() fails to trigger DatabaseArray
+    loading. This patch pre-accesses the array to force loading before get_value().
+    See: .claude/click-selection-bug-fix.md for details.
+    """
+    from motile_tracker.data_views.views.layers.track_labels import TrackLabels
+
+    # Store original __init__
+    _original_init = TrackLabels.__init__
+
+    def patched_init(self, viewer, data, name, opacity, scale, tracks_viewer):
+        # Call original __init__ which sets up the original click callback
+        _original_init(self, viewer, data, name, opacity, scale, tracks_viewer)
+
+        # Remove the original click callback (it's the last one added)
+        if self.mouse_drag_callbacks:
+            self.mouse_drag_callbacks.pop()
+
+        # Add our fixed click callback
+        @self.mouse_drag_callbacks.append
+        def fixed_click(layer, event):
+            if (
+                event.type == "mouse_press"
+                and layer.mode == "pan_zoom"
+                and not (
+                    layer.tracks_viewer.mode == "lineage"
+                    and layer.viewer.dims.ndisplay == 3
+                )
+            ):
+                # WORKAROUND: Pre-access array to trigger DatabaseArray loading
+                # Without this, get_value() fails after colormap updates
+                data_coords = layer.world_to_data(event.position)
+                try:
+                    t_idx = int(data_coords[0])
+                    # Access time slice to ensure DatabaseArray.fill_array() is called
+                    _ = layer.data[t_idx]
+                except Exception:
+                    pass  # If this fails, get_value() will also fail
+
+                label = layer.get_value(
+                    event.position,
+                    view_direction=event.view_direction,
+                    dims_displayed=event.dims_displayed,
+                    world=True,
+                )
+
+                if (
+                    label is not None
+                    and label != 0
+                    and layer.colormap.map(label)[-1] != 0
+                ):
+                    append = "Shift" in event.modifiers
+                    layer.tracks_viewer.selected_nodes.add(label, append)
+
+    # Replace TrackLabels.__init__ with patched version
+    TrackLabels.__init__ = patched_init
+
+
+# Apply the patch
+patch_track_labels_click_handler()
+
+
 # def get_status(self, position, view_direction=None, dims_displayed=None, world=True):
 #     return "True" #works to allow napari grid view, but not for cursor position/value display
 # TrackLabels.get_status = get_status

From faa79aeef9927da4e66aa2ac1ed6369be83b31d7 Mon Sep 17 00:00:00 2001
From: TeunHuijben <teun.huijben@czbiohub.org>
Date: Tue, 10 Feb 2026 15:55:42 -0800
Subject: [PATCH 04/15] add claude to gitignore

---
 .gitignore | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.gitignore b/.gitignore
index dc80cdd..bdaf5ab 100644
--- a/.gitignore
+++ b/.gitignore
@@ -168,6 +168,7 @@ cython_debug/
 .history
 .vscode/
 CLAUDE.md
+.claude
 
 # Ignore examples files
 examples/*/*

From 49cb28701ddd56478b7791910869b066d5cb5e40 Mon Sep 17 00:00:00 2001
From: TeunHuijben <teun.huijben@czbiohub.org>
Date: Wed, 11 Feb 2026 10:11:53 -0800
Subject: [PATCH 05/15] instanseg inference on the spot

---
 scripts/script_instanseg.py              |  68 ++++
 trackedit/TrackEditClass.py              | 352 ++++++++++++++++++-
 trackedit/instanseg_inference.py         | 418 +++++++++++++++++++++++
 trackedit/run.py                         |  30 ++
 trackedit/utils/red_flag_funcs.py        |   3 +-
 trackedit/widgets/CustomEditingWidget.py |  36 +-
 6 files changed, 893 insertions(+), 14 deletions(-)
 create mode 100644 scripts/script_instanseg.py
 create mode 100644 trackedit/instanseg_inference.py

diff --git a/scripts/script_instanseg.py b/scripts/script_instanseg.py
new file mode 100644
index 0000000..0e01e14
--- /dev/null
+++ b/scripts/script_instanseg.py
@@ -0,0 +1,68 @@
+import sys
+import warnings
+from pathlib import Path
+
+import numpy as np
+
+from trackedit.run import run_trackedit
+
+# Databases saved with numpy>2 need np._core.numeric, which is not available in numpy<2, hence the following hack
+sys.modules["numpy._core.numeric"] = np.core.numeric
+
+warnings.filterwarnings("ignore", category=FutureWarning, message=".*qt_viewer.*")
+
+# **********INPUTS*********
+# path to the working directory that contains the database file AND metadata.toml:
+working_directory = Path("/home/teun.huijben/Documents/data/Thibaut/masks_on_geff/")
+# name of the database file to start from, or "latest" to start from the latest version, defaults to "data.db"
+db_filename_start = "latest"
+# maximum number of frames display, defaults to None (use all frames)
+tmax = 100
+# (Z),Y,X, defaults to (1, 1, 1)
+scale = (1.625, 0.40625, 0.40625)
+# overwrite existing database/changelog, defaults to False (not used when db_filename_start is "latest")
+allow_overwrite = False
+
+# OPTIONAL: imaging data
+imaging_zarr_file = (
+    "/hpc/projects/group.royer/people/teun.huijben/data/Thibault/4th_exp/first_fov.zarr"
+)
+imaging_channel = "0"
+imaging_layer_names = ["dense", "sparse"]
+
+# OPTIONAL: annotation mapping (default is neuromast cell types)
+# annotation_mapping = {
+#     1: {"name": "hair", "color": [0.0, 1.0, 0.0, 1.0]},  # green
+#     2: {"name": "support", "color": [1.0, 0.1, 0.6, 1.0]},  # pink
+#     3: {"name": "mantle", "color": [0.0, 0.0, 0.9, 1.0]},  # blue
+# }
+annotation_mapping = None
+
+# OPTIONAL: InstanSeg model for interactive cell segmentation
+# Enable this to add cells via InstanSeg inference instead of spherical masks
+flag_allow_adding_instanseg_cell = True
+instanseg_model_path = (
+    "/hpc/projects/group.royer/people/teun.huijben/data/Thibault/model_96.pt"
+)
+instanseg_device = None  # 'cuda', 'cpu', or None for auto-detect
+# *************************
+
+if __name__ == "__main__":
+    run_trackedit(
+        working_directory=working_directory,
+        db_filename=db_filename_start,
+        tmax=tmax,
+        scale=scale,
+        allow_overwrite=allow_overwrite,
+        imaging_zarr_file=imaging_zarr_file,
+        imaging_channel=imaging_channel,
+        imaging_layer_names=imaging_layer_names,
+        annotation_mapping=annotation_mapping,
+        flag_allow_adding_spherical_cell=True,
+        adding_spherical_cell_radius=10,
+        flag_remove_red_flags_at_edge=True,
+        remove_red_flags_at_edge_threshold=10,
+        flag_allow_adding_instanseg_cell=flag_allow_adding_instanseg_cell,
+        instanseg_model_path=instanseg_model_path,
+        instanseg_device=instanseg_device,
+    )
diff --git a/trackedit/TrackEditClass.py b/trackedit/TrackEditClass.py
index f08f222..3714373 100644
--- a/trackedit/TrackEditClass.py
+++ b/trackedit/TrackEditClass.py
@@ -25,11 +25,14 @@ def __init__(
         flag_show_hierarchy: bool = True,
         flag_allow_adding_spherical_cell: bool = False,
         adding_spherical_cell_radius: int = 10,
+        flag_allow_adding_instanseg_cell: bool = False,
+        instanseg_inference=None,
     ):
         self.viewer = viewer
         self.viewer.layers.clear()  # Remove all existing layers
         self.databasehandler = databasehandler
         self.flag_show_hierarchy = flag_show_hierarchy
+        self.instanseg_inference = instanseg_inference
 
         self.tracksviewer = TracksViewer.get_instance(self.viewer)
 
@@ -41,6 +44,7 @@ def __init__(
             self.databasehandler,
             allow_adding_spherical_cell=flag_allow_adding_spherical_cell,
             adding_spherical_cell_radius=adding_spherical_cell_radius,
+            allow_adding_instanseg_cell=flag_allow_adding_instanseg_cell,
         )
 
         tabwidget_right = QTabWidget()
@@ -98,7 +102,9 @@ def __init__(
                     colormap=colormap,
                     opacity=opacity,
                     scale=self.databasehandler.scale,
-                    visible=False,
+                    visible=True
+                    if i == 0
+                    else False,  # Show only first channel by default
                     translate=self.databasehandler.image_translate,
                 )
                 layer.reset_contrast_limits()
@@ -130,11 +136,19 @@ def __init__(
         # Connect spherical cell signal if feature is enabled
         if flag_allow_adding_spherical_cell:
             self.EditingMenu.add_spherical_cell_toggled.connect(
-                self._toggle_add_cell_mode
+                self._toggle_add_spherical_cell_mode
             )
             # Initialize spherical cell mode flag
             self._add_cell_mode_active = False
 
+        # Connect InstanSeg cell signal if feature is enabled
+        if flag_allow_adding_instanseg_cell:
+            self.EditingMenu.add_instanseg_cell_toggled.connect(
+                self._toggle_add_instanseg_mode
+            )
+            # Initialize InstanSeg cell mode flag
+            self._add_instanseg_mode_active = False
+
         self.add_tracks()
         self.NavigationWidget.time_box.update_chunk_label()
         self.NavigationWidget.red_flag_box.update_red_flag_counter_and_info()
@@ -409,7 +423,6 @@ def duplicate_cell_from_database(self, node_id: int, time: int):
                 bbox=pickle.bbox,
                 include_overlaps=True,
             )
-            print("added node to db:", new_id, "at time", time)
 
             max_track_id = max(
                 self.NavigationWidget.tracks_viewer.tracks_controller.tracks.track_id_to_node.keys()
@@ -511,31 +524,39 @@ def add_spherical_cell_at_position(self, position_scaled, radius_pixels=10):
                 0,
                 lambda: (
                     self.EditingMenu.add_spherical_cell_btn.setChecked(False),
-                    self._toggle_add_cell_mode(False),
+                    self._toggle_add_spherical_cell_mode(False),
                 ),
             )
 
         return new_node_id
 
-    def _toggle_add_cell_mode(self, checked):
+    def _toggle_add_spherical_cell_mode(self, checked):
         """Toggle the add spherical cell mode on/off."""
         self._add_cell_mode_active = checked
 
         if checked:
             # Only add callback if not already present
-            if self._on_mouse_click_add_cell not in self.viewer.mouse_drag_callbacks:
-                self.viewer.mouse_drag_callbacks.append(self._on_mouse_click_add_cell)
+            if (
+                self._on_mouse_click_add_spherical_cell
+                not in self.viewer.mouse_drag_callbacks
+            ):
+                self.viewer.mouse_drag_callbacks.append(
+                    self._on_mouse_click_add_spherical_cell
+                )
         else:
             # Remove ALL instances of the callback (in case of duplicates)
-            while self._on_mouse_click_add_cell in self.viewer.mouse_drag_callbacks:
+            while (
+                self._on_mouse_click_add_spherical_cell
+                in self.viewer.mouse_drag_callbacks
+            ):
                 try:
                     self.viewer.mouse_drag_callbacks.remove(
-                        self._on_mouse_click_add_cell
+                        self._on_mouse_click_add_spherical_cell
                     )
                 except ValueError:
                     break
 
-    def _on_mouse_click_add_cell(self, viewer, event):
+    def _on_mouse_click_add_spherical_cell(self, viewer, event):
         """Handle mouse click when add cell mode is active.
 
         Called when user clicks in viewer while add cell mode is on.
@@ -563,3 +584,314 @@ def _on_mouse_click_add_cell(self, viewer, event):
 
             # Yield to prevent further event propagation
             yield
+
+    def _toggle_add_instanseg_mode(self, checked):
+        """Toggle the add InstanSeg cell mode on/off."""
+        self._add_instanseg_mode_active = checked
+
+        if checked:
+            # Mutual exclusion: disable spherical mode if active
+            if hasattr(self, "_add_cell_mode_active") and self._add_cell_mode_active:
+                if hasattr(self.EditingMenu, "add_spherical_cell_btn"):
+                    self.EditingMenu.add_spherical_cell_btn.setChecked(False)
+
+            # Only add callback if not already present
+            if (
+                self._on_mouse_click_add_instanseg
+                not in self.viewer.mouse_drag_callbacks
+            ):
+                self.viewer.mouse_drag_callbacks.append(
+                    self._on_mouse_click_add_instanseg
+                )
+        else:
+            # Remove ALL instances of the callback (in case of duplicates)
+            while (
+                self._on_mouse_click_add_instanseg in self.viewer.mouse_drag_callbacks
+            ):
+                try:
+                    self.viewer.mouse_drag_callbacks.remove(
+                        self._on_mouse_click_add_instanseg
+                    )
+                except ValueError:
+                    break
+
+    def _on_mouse_click_add_instanseg(self, viewer, event):
+        """Handle mouse click when InstanSeg add cell mode is active.
+
+        Called when user clicks in viewer while InstanSeg mode is on.
+        """
+        # Guard: only proceed if mode is actually active
+        if not self._add_instanseg_mode_active:
+            return
+
+        # Only trigger on click (not drag)
+        if event.type == "mouse_press":
+            # Get click position and time
+            position = viewer.cursor.position
+            current_time = int(position[0])
+
+            # Add InstanSeg cell at clicked position (pass viewer for ray casting)
+            self.add_instanseg_cell_at_position(viewer, current_time)
+
+            # Yield to prevent further event propagation
+            yield
+
+    def add_instanseg_cell_at_position(self, viewer, current_time):
+        """Add a new cell using InstanSeg segmentation at the clicked position.
+
+        Uses ray casting to find the brightest pixel along the camera ray,
+        then runs InstanSeg inference with that point as a seed.
+
+        Parameters
+        ----------
+        viewer : napari.Viewer
+            The napari viewer instance
+        current_time : int
+            Current time frame
+
+        Returns
+        -------
+        new_node_id : int or None
+            Database ID of the newly created node, or None if failed
+        """
+        # Verify imaging data is available
+        if not self.databasehandler.imaging_flag:
+            show_warning("InstanSeg requires imaging data. No imaging data loaded.")
+            return None
+
+        # Verify InstanSeg inference is available
+        if self.instanseg_inference is None:
+            show_warning("InstanSeg inference engine not initialized.")
+            return None
+
+        # Update chunk if needed
+        self.update_chunk_from_frame(current_time)
+
+        # Get image at current time - use sparse channel (index 1)
+        image_volume = self.databasehandler.imagingArray.get_channel_data(1)[
+            current_time
+        ]  # (Z, Y, X) or (Y, X)
+
+        # Convert dask array to numpy array if needed
+        if hasattr(image_volume, "compute"):
+            image_volume = image_volume.compute()
+        image_volume = np.asarray(image_volume)
+
+        # Get view direction for ray casting
+        vd_world = getattr(viewer.cursor, "_view_direction", None)
+        if vd_world is None or np.allclose(vd_world, 0):
+            show_warning(
+                "Could not get view direction from napari cursor. Try clicking again."
+            )
+            return None
+
+        # Get cursor position in world coordinates
+        cursor_pos_world = np.asarray(viewer.cursor.position, dtype=float)
+
+        # Extract spatial position (remove time dimension)
+        if self.databasehandler.ndim == 4:
+            cursor_spatial_world = cursor_pos_world[1:]  # (z, y, x)
+            scale_array = np.array(
+                [
+                    self.databasehandler.z_scale,
+                    self.databasehandler.y_scale,
+                    self.databasehandler.x_scale,
+                ]
+            )
+        else:
+            cursor_spatial_world = cursor_pos_world[1:]  # (y, x)
+            scale_array = np.array(
+                [self.databasehandler.y_scale, self.databasehandler.x_scale]
+            )
+
+        # Convert view direction and cursor position to data coordinates
+        vd_world = np.asarray(vd_world, dtype=float)
+        # View direction is also 4D (time + spatial), extract only spatial components
+        vd_spatial_world = vd_world[1:] if len(vd_world) == 4 else vd_world
+        vd_data = vd_spatial_world / scale_array
+        norm = np.linalg.norm(vd_data)
+        if norm == 0:
+            show_warning("Invalid view direction. Try clicking again.")
+            return None
+        vd_data /= norm
+
+        origin_data = cursor_spatial_world / scale_array
+
+        # Cast ray through volume in both directions
+        diag = int(np.sqrt(sum(s**2 for s in image_volume.shape)))
+        t_values = np.arange(-diag, diag + 1, dtype=float)
+        ray_points = origin_data[None, :] + t_values[:, None] * vd_data[None, :]
+        ray_voxels = np.round(ray_points).astype(int)
+
+        # Keep only voxels inside the volume
+        if self.databasehandler.ndim == 4:
+            valid = (
+                (ray_voxels[:, 0] >= 0)
+                & (ray_voxels[:, 0] < image_volume.shape[0])
+                & (ray_voxels[:, 1] >= 0)
+                & (ray_voxels[:, 1] < image_volume.shape[1])
+                & (ray_voxels[:, 2] >= 0)
+                & (ray_voxels[:, 2] < image_volume.shape[2])
+            )
+        else:
+            valid = (
+                (ray_voxels[:, 0] >= 0)
+                & (ray_voxels[:, 0] < image_volume.shape[0])
+                & (ray_voxels[:, 1] >= 0)
+                & (ray_voxels[:, 1] < image_volume.shape[1])
+            )
+
+        ray_voxels = ray_voxels[valid]
+        if len(ray_voxels) == 0:
+            show_warning("Ray does not intersect volume. Try clicking inside the data.")
+            return None
+
+        # Deduplicate and find voxel with maximum intensity (brightest pixel)
+        ray_voxels = np.unique(ray_voxels, axis=0)
+        if self.databasehandler.ndim == 4:
+            intensities = image_volume[
+                ray_voxels[:, 0], ray_voxels[:, 1], ray_voxels[:, 2]
+            ]
+        else:
+            intensities = image_volume[ray_voxels[:, 0], ray_voxels[:, 1]]
+
+        best_idx = int(np.argmax(intensities))
+        best_voxel = ray_voxels[best_idx]
+        position_data = tuple(best_voxel.astype(float))
+
+        # Prepare scale tuple and handle 2D vs 3D
+        if self.databasehandler.ndim == 4:
+            # 3D data: need (z_scale, y_scale, x_scale) in microns
+            scale = (
+                self.databasehandler.z_scale,
+                self.databasehandler.y_scale,
+                self.databasehandler.x_scale,
+            )
+        else:
+            # 2D data: InstanSeg expects 3D, add singleton z dimension
+            image_volume = image_volume[np.newaxis, ...]  # (1, Y, X)
+            position_data = (0,) + position_data  # (0, y, x)
+            # Use y_scale as z_scale for isotropic rescaling
+            scale = (
+                self.databasehandler.y_scale,
+                self.databasehandler.y_scale,
+                self.databasehandler.x_scale,
+            )
+
+        # Run InstanSeg inference
+        try:
+            mask, bbox = self.instanseg_inference.run_inference_at_position(
+                image_volume=image_volume,
+                time_frame=current_time,
+                position=position_data,
+                scale=scale,
+            )
+        except RuntimeError as e:
+            if "CUDA out of memory" in str(e):
+                show_warning(
+                    "GPU out of memory! Volume is too large after rescaling to isotropic. "
+                    "Solutions: (1) Close other GPU apps, (2) Set instanseg_device='cpu' "
+                    "in script (slower but uses less memory), or (3) Process smaller regions."
+                )
+            else:
+                show_warning(f"InstanSeg inference failed: {str(e)}")
+            import traceback
+
+            traceback.print_exc()
+            return None
+        except Exception as e:
+            show_warning(f"InstanSeg inference failed: {str(e)}")
+            import traceback
+
+            traceback.print_exc()
+            return None
+
+        if mask is None:
+            show_warning("InstanSeg could not segment a cell at this position.")
+            return None
+
+        # Remove singleton z dimension for 2D data
+        if self.databasehandler.ndim == 3:
+            mask = mask.squeeze(0)  # (Y, X)
+            bbox = (bbox[0][1:], bbox[1][1:])  # Remove z from bbox
+
+        # Add to database
+        try:
+            new_node_id = add_new_node(
+                self.databasehandler.config_adjusted,
+                time=current_time,
+                mask=mask,
+                bbox=bbox,
+                include_overlaps=True,
+            )
+            fix_overlap_ancestor_ids(
+                database_path=self.databasehandler.config_adjusted.data_config.database_path,
+                new_node_id=new_node_id,
+                current_time=current_time,
+            )
+        except Exception as e:
+            show_warning(f"Failed to add node to database: {e}")
+            import traceback
+
+            traceback.print_exc()
+            return None
+
+        # Add to tracking system
+        track_ids = (
+            self.NavigationWidget.tracks_viewer.tracks_controller.tracks.track_id_to_node.keys()
+        )
+        max_track_id = max(track_ids) if track_ids else 0
+        time_in_chunk = current_time - self.databasehandler.time_window[0]
+
+        attributes = {
+            NodeAttr.TIME.value: [time_in_chunk],
+            NodeAttr.TRACK_ID.value: [max_track_id + 1],
+            "node_id": [new_node_id],
+        }
+        self.tracksviewer.tracks_controller.add_nodes(
+            attributes, [(np.array([0, 0, 0]))]
+        )
+
+        # Refresh and auto-disable
+        show_info(f"Added InstanSeg cell with ID {new_node_id}")
+        self.databasehandler.segments.force_refill()
+        self.viewer.layers[self.databasehandler.name + "_seg"].refresh()
+
+        # Auto-disable only if button exists
+        if hasattr(self.EditingMenu, "add_instanseg_cell_btn"):
+            from qtpy.QtCore import QTimer
+
+            QTimer.singleShot(
+                0,
+                lambda: (
+                    self.EditingMenu.add_instanseg_cell_btn.setChecked(False),
+                    self._toggle_add_instanseg_mode(False),
+                ),
+            )
+
+        return new_node_id
+
+    def _convert_viewer_to_data_coords(self, position_spatial: tuple) -> tuple:
+        """Convert viewer-scaled coordinates to database data coordinates.
+
+        Parameters
+        ----------
+        position_spatial : tuple
+            Position in viewer coordinates (z, y, x) or (y, x), scaled
+
+        Returns
+        -------
+        tuple
+            Position in data coordinates (z, y, x) or (y, x), unscaled
+        """
+        if self.databasehandler.ndim == 4:
+            # 4D: (z, y, x) viewer -> (z, y, x) data
+            z_unscaled = position_spatial[0] / self.databasehandler.z_scale
+            y_unscaled = position_spatial[1] / self.databasehandler.y_scale
+            x_unscaled = position_spatial[2] / self.databasehandler.x_scale
+            return (z_unscaled, y_unscaled, x_unscaled)
+        else:
+            # 3D: (y, x) viewer -> (y, x) data
+            y_unscaled = position_spatial[0] / self.databasehandler.y_scale
+            x_unscaled = position_spatial[1] / self.databasehandler.x_scale
+            return (y_unscaled, x_unscaled)
diff --git a/trackedit/instanseg_inference.py b/trackedit/instanseg_inference.py
new file mode 100644
index 0000000..6c0561a
--- /dev/null
+++ b/trackedit/instanseg_inference.py
@@ -0,0 +1,418 @@
+"""InstanSeg inference integration for TrackEdit.
+
+This module provides interactive cell segmentation using InstanSeg models.
+It manages model loading, embedding caching, and inference execution for
+click-based cell addition.
+"""
+
+from pathlib import Path
+from typing import Optional, Tuple
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from napari.utils.notifications import show_warning
+
+# Target pixel size for isotropic rescaling (must match training)
+TARGET_PIXEL_SIZE = 0.5
+
+
+def normalize(image: np.ndarray) -> np.ndarray:
+    """Percentile-normalize a volume to [0, 1]."""
+    data = image.astype(np.float32)
+    low = np.percentile(data, 0.1)
+    high = np.percentile(data, 99.9)
+    if high > low:
+        normalized = (data - low) / (high - low)
+        # Clamp to [0, 1] to handle outliers above 99.9th percentile
+        return np.clip(normalized, 0, 1)
+    return data / (data.max() + 1e-8)
+
+
+def rescale_to_isotropic(
+    image: np.ndarray,
+    current_scale: tuple,
+    target_pixel_size: float = TARGET_PIXEL_SIZE,
+    device: str = "cpu",
+) -> Tuple[np.ndarray, tuple]:
+    """Rescale a (Z, Y, X) volume to isotropic resolution using trilinear interpolation.
+
+    Args:
+        image: Input volume (Z, Y, X)
+        current_scale: Current voxel sizes (z_size, y_size, x_size) in microns
+        target_pixel_size: Target isotropic pixel size in microns
+        device: Device to run interpolation on ('cuda' or 'cpu')
+
+    Returns:
+        (rescaled_image_np, scale_factors) where scale_factors = (sz, sy, sx)
+    """
+    sz = current_scale[0] / target_pixel_size
+    sy = current_scale[1] / target_pixel_size
+    sx = current_scale[2] / target_pixel_size
+
+    d, h, w = image.shape
+    new_d = int(round(d * sz))
+    new_h = int(round(h * sy))
+    new_w = int(round(w * sx))
+
+    tensor = torch.from_numpy(image).float().unsqueeze(0).unsqueeze(0)  # (1,1,Z,Y,X)
+    if device == "cuda" and torch.cuda.is_available():
+        tensor = tensor.to(device)
+    rescaled = F.interpolate(
+        tensor, size=(new_d, new_h, new_w), mode="trilinear", align_corners=False
+    )
+    if device == "cuda":
+        rescaled = rescaled.cpu()
+    return rescaled.squeeze().numpy(), (sz, sy, sx)
+
+
+def rescale_labels_back(labels: np.ndarray, original_shape: tuple) -> np.ndarray:
+    """Rescale labels from isotropic back to original shape using nearest interpolation."""
+    tensor = torch.from_numpy(labels.astype(np.float32)).unsqueeze(0).unsqueeze(0)
+    rescaled = F.interpolate(tensor, size=original_shape, mode="nearest")
+    return rescaled.squeeze().numpy().astype(np.int32)
+
+
+def run_backbone(model, image_normalized: np.ndarray, device: str) -> torch.Tensor:
+    """Run only the backbone. Returns (C, D, H, W) prediction tensor on GPU."""
+    tensor = torch.from_numpy(image_normalized).float()
+    if tensor.ndim == 3:
+        tensor = tensor.unsqueeze(0).unsqueeze(0)  # (1, 1, Z, Y, X)
+    tensor = tensor.to(device)
+    with torch.no_grad():
+        pred = model.backbone(tensor)  # (1, C, D, H, W)
+    return pred[0]  # (C, D, H, W), stays on device
+
+
+def run_postprocessing(
+    model,
+    cached_pred: torch.Tensor,
+    precomputed_seeds: Optional[torch.Tensor] = None,
+    seed_threshold: float = 0.5,
+    mask_threshold: float = 0.5,
+    overlap_threshold: float = 1.0,
+    pairwise_filter_threshold: float = 0.0,
+    peak_distance: int = 4,
+    window_size: int = 64,
+    use_iom: bool = False,
+    use_pairwise_seed_filter: bool = False,
+    cleanup_fragments: bool = True,
+) -> np.ndarray:
+    """Run only the postprocessing on a cached prediction.
+
+    Args:
+        model: InstanSeg TorchScript model
+        cached_pred: Cached backbone embeddings (C, D, H, W) on GPU
+        precomputed_seeds: Optional seed points in isotropic coordinates (N, 3)
+        seed_threshold: Threshold for seed detection
+        mask_threshold: Threshold for mask generation
+        overlap_threshold: Threshold for overlap detection
+        pairwise_filter_threshold: Threshold for pairwise filtering
+        peak_distance: Distance for peak detection
+        window_size: Size of processing window
+        use_iom: Use intersection over minimum instead of IOU
+        use_pairwise_seed_filter: Enable pairwise seed filtering
+        cleanup_fragments: Remove small fragments
+
+    Returns:
+        Instance labels (Z, Y, X) as int32 numpy array
+    """
+    # Prepare seeds if provided
+    seeds = None
+    if precomputed_seeds is not None and precomputed_seeds.shape[0] > 0:
+        seeds = precomputed_seeds.to(cached_pred.device)
+
+    with torch.no_grad():
+        labels = model.postprocessing(
+            cached_pred,
+            seed_threshold=seed_threshold,
+            mask_threshold=mask_threshold,
+            overlap_threshold=overlap_threshold,
+            pairwise_filter_threshold=pairwise_filter_threshold,
+            peak_distance=peak_distance,
+            window_size=window_size,
+            use_iom=use_iom,
+            use_pairwise_seed_filter=use_pairwise_seed_filter,
+            cleanup_fragments=cleanup_fragments,
+            precomputed_seeds=seeds if seeds is not None else torch.empty(0),
+        )  # (1, D, H, W)
+
+    return labels.squeeze().cpu().numpy().astype(np.int32)
+
+
+def extract_mask_at_seed(
+    labels: np.ndarray, seed_position: tuple
+) -> Tuple[Optional[np.ndarray], Optional[tuple]]:
+    """Extract the instance mask at seed position and compute its bounding box.
+
+    Args:
+        labels: Instance segmentation (Z, Y, X) with unique IDs per cell
+        seed_position: (z, y, x) position in label coordinates
+
+    Returns:
+        (mask, bbox) where:
+        - mask: Binary mask (Z, Y, X) of the cell at seed, or None if no cell
+        - bbox: Bounding box ((z_min, y_min, x_min), (z_max, y_max, x_max)), or None
+    """
+    z, y, x = seed_position
+    z, y, x = int(round(z)), int(round(y)), int(round(x))
+
+    # Check bounds
+    if not (
+        0 <= z < labels.shape[0]
+        and 0 <= y < labels.shape[1]
+        and 0 <= x < labels.shape[2]
+    ):
+        return None, None
+
+    # Get label ID at seed position
+    label_id = labels[z, y, x]
+
+    if label_id == 0:
+        # No cell at this position
+        return None, None
+
+    # Extract binary mask for this instance
+    mask = (labels == label_id).astype(bool)
+
+    # Return the full mask (will be cropped after rescaling to original resolution)
+    # Don't crop here because we need to rescale the full mask first
+    return mask, None
+
+
+class InstanSegInference:
+    """Manages InstanSeg model and inference for TrackEdit.
+
+    This class provides interactive cell segmentation by:
+    1. Loading a TorchScript InstanSeg model
+    2. Caching embeddings per time frame for fast repeated inference
+    3. Running seed-based postprocessing on cached embeddings
+    4. Extracting single-cell masks from instance segmentation
+    """
+
+    def __init__(
+        self,
+        model_path: str,
+        device: Optional[str] = None,
+        cache_size: int = 3,
+    ):
+        """Initialize InstanSeg inference engine.
+
+        Args:
+            model_path: Path to TorchScript (.pt) model file
+            device: Device for inference ('cuda', 'cpu', or None for auto)
+            cache_size: Maximum number of frames to cache embeddings for
+        """
+        model_path = Path(model_path)
+        if not model_path.exists():
+            raise FileNotFoundError(f"Model not found: {model_path}")
+
+        if device is None:
+            device = "cuda" if torch.cuda.is_available() else "cpu"
+
+        self.device = device
+        self.cache_size = cache_size
+
+        # Load model
+        self.model = torch.jit.load(str(model_path), map_location=device)
+        self.model.eval()
+
+        # Cache structure: {time: (embeddings_tensor, iso_shape, scale_factors, original_shape)}
+        self.cached_embeddings = {}
+        self.cache_order = []  # Track insertion order for LRU eviction
+
+        # Verify GPU setup
+        if device == "cuda":
+            if torch.cuda.is_available():
+                gpu_name = torch.cuda.get_device_name(0)
+                gpu_memory = torch.cuda.get_device_properties(0).total_memory / 1e9
+                print(f"✓ InstanSeg using GPU: {gpu_name} ({gpu_memory:.1f} GB)")
+            else:
+                print(
+                    "⚠ Warning: CUDA requested but not available, falling back to CPU"
+                )
+                self.device = "cpu"
+        else:
+            print(f"InstanSeg using device: {device}")
+
+    def run_inference_at_position(
+        self,
+        image_volume: np.ndarray,
+        time_frame: int,
+        position: tuple,
+        scale: tuple,
+    ) -> Tuple[Optional[np.ndarray], Optional[tuple]]:
+        """Run InstanSeg inference with seed at position.
+
+        Args:
+            image_volume: Raw image volume (Z, Y, X) at original resolution
+            time_frame: Time frame index (for caching)
+            position: (z, y, x) seed position in original data coordinates
+            scale: (z_scale, y_scale, x_scale) voxel sizes in microns
+
+        Returns:
+            (mask, bbox) tuple:
+            - mask: Binary mask (Z, Y, X) at original resolution, or None if no cell
+            - bbox: Bounding box ((z_min, y_min, x_min), (z_max, y_max, x_max)), or None
+        """
+        try:
+            # Normalize image
+            image_normalized = normalize(image_volume)
+
+            # Rescale to isotropic resolution (on GPU if available)
+            image_isotropic, scale_factors = rescale_to_isotropic(
+                image_normalized, scale, TARGET_PIXEL_SIZE, device=self.device
+            )
+            iso_shape = image_isotropic.shape
+            original_shape = image_volume.shape
+
+            # Check cache for embeddings
+            if time_frame in self.cached_embeddings:
+                embeddings, cached_iso_shape, _, _ = self.cached_embeddings[time_frame]
+
+                # Verify shape matches (in case scale changed)
+                if cached_iso_shape != iso_shape:
+                    embeddings = self._compute_and_cache_embeddings(
+                        time_frame,
+                        image_isotropic,
+                        iso_shape,
+                        scale_factors,
+                        original_shape,
+                    )
+            else:
+                # Run backbone and cache
+                embeddings = self._compute_and_cache_embeddings(
+                    time_frame,
+                    image_isotropic,
+                    iso_shape,
+                    scale_factors,
+                    original_shape,
+                )
+
+            # Convert position to isotropic coordinates
+            position_iso = self._convert_to_isotropic_coords(
+                position, original_shape, iso_shape
+            )
+
+            # Run postprocessing with seed
+            seed_tensor = torch.from_numpy(np.array([position_iso])).float()  # (1, 3)
+
+            labels_isotropic = run_postprocessing(
+                self.model,
+                embeddings,
+                precomputed_seeds=seed_tensor,
+                seed_threshold=0.5,
+                mask_threshold=0.5,
+                overlap_threshold=1.0,
+                pairwise_filter_threshold=0.0,
+                peak_distance=4,
+                window_size=64,
+                use_iom=False,
+                use_pairwise_seed_filter=False,
+                cleanup_fragments=True,
+            )
+
+            # Extract single-instance mask at seed
+            mask_isotropic, _ = extract_mask_at_seed(labels_isotropic, position_iso)
+
+            if mask_isotropic is None:
+                show_warning("No cell detected at clicked position")
+                return None, None
+
+            # Rescale mask back to original resolution
+            mask_original = rescale_labels_back(mask_isotropic, original_shape)
+            mask_binary = (mask_original > 0).astype(bool)
+
+            # Compute bounding box in original coordinates
+            coords = np.argwhere(mask_binary > 0)
+            if len(coords) == 0:
+                return None, None
+
+            bbox_min = coords.min(axis=0)
+            bbox_max = coords.max(axis=0) + 1
+
+            # Crop mask to bbox region (mask must match bbox shape)
+            mask_cropped = mask_binary[
+                bbox_min[0] : bbox_max[0],
+                bbox_min[1] : bbox_max[1],
+                bbox_min[2] : bbox_max[2],
+            ]
+
+            # Flatten to [z_min, y_min, x_min, z_max, y_max, x_max]
+            bbox = np.concatenate([bbox_min, bbox_max]).astype(np.int32)
+
+            return mask_cropped, bbox
+
+        except Exception as e:
+            show_warning(f"InstanSeg inference failed: {str(e)}")
+            import traceback
+
+            traceback.print_exc()
+            return None, None
+
+    def _compute_and_cache_embeddings(
+        self,
+        time_frame: int,
+        image_isotropic: np.ndarray,
+        iso_shape: tuple,
+        scale_factors: tuple,
+        original_shape: tuple,
+    ) -> torch.Tensor:
+        """Compute backbone embeddings and add to cache.
+
+        Args:
+            time_frame: Time frame index
+            image_isotropic: Normalized isotropic image (Z, Y, X)
+            iso_shape: Shape of isotropic volume
+            scale_factors: Scale factors used for rescaling
+            original_shape: Original image shape
+
+        Returns:
+            Embeddings tensor (C, D, H, W) on GPU
+        """
+        # Run backbone inference
+        embeddings = run_backbone(self.model, image_isotropic, self.device)
+
+        # Report GPU memory usage if available
+        if self.device == "cuda" and torch.cuda.is_available():
+            gpu_mem_mb = torch.cuda.memory_allocated() / 1e6
+            print(f"GPU memory: {gpu_mem_mb:.0f} MB")
+
+        # Add to cache
+        self.cached_embeddings[time_frame] = (
+            embeddings,
+            iso_shape,
+            scale_factors,
+            original_shape,
+        )
+        self.cache_order.append(time_frame)
+
+        # Evict oldest if cache full
+        if len(self.cache_order) > self.cache_size:
+            oldest_time = self.cache_order.pop(0)
+            if oldest_time in self.cached_embeddings:
+                del self.cached_embeddings[oldest_time]
+
+        return embeddings
+
+    def _convert_to_isotropic_coords(
+        self, position: tuple, original_shape: tuple, iso_shape: tuple
+    ) -> tuple:
+        """Convert position from original to isotropic coordinates.
+
+        Args:
+            position: (z, y, x) in original coordinates
+            original_shape: Original volume shape
+            iso_shape: Isotropic volume shape
+
+        Returns:
+            (z_iso, y_iso, x_iso) in isotropic coordinates
+        """
+        scale = np.array(iso_shape) / np.array(original_shape)
+        position_iso = np.array(position) * scale
+        return tuple(position_iso)
+
+    def clear_cache(self):
+        """Clear all cached embeddings."""
+        self.cached_embeddings.clear()
+        self.cache_order.clear()
diff --git a/trackedit/run.py b/trackedit/run.py
index 529b238..7b2f4bb 100644
--- a/trackedit/run.py
+++ b/trackedit/run.py
@@ -41,6 +41,9 @@ def run_trackedit(
     flag_show_hierarchy: bool = True,
     flag_allow_adding_spherical_cell: bool = False,
     adding_spherical_cell_radius: int = 10,
+    flag_allow_adding_instanseg_cell: bool = False,
+    instanseg_model_path: Optional[str] = None,
+    instanseg_device: Optional[str] = None,
     flag_remove_red_flags_at_edge: bool = False,
     remove_red_flags_at_edge_threshold: int = 10,
     annotation_mapping: Optional[dict] = None,
@@ -66,6 +69,11 @@ def run_trackedit(
         viewer: Optional existing napari viewer
         flag_show_hierarchy: Show hierarchy in the viewer
         flag_allow_adding_spherical_cell: Allow adding spherical cells via button (default: False)
+        adding_spherical_cell_radius: Radius of spherical cells in pixels (default: 10)
+        flag_allow_adding_instanseg_cell: Allow adding InstanSeg-segmented cells via button (default: False)
+        instanseg_model_path: Path to InstanSeg TorchScript model file
+            (required if flag_allow_adding_instanseg_cell=True)
+        instanseg_device: Device for InstanSeg inference ('cuda', 'cpu', or None for auto-detect)
         annotation_mapping: Mapping of annotation ids to names and colors
         imaging_layer_names: Names for imaging layers. If None, defaults to ['nuclear', 'membrane'] for 2 channels
 
@@ -99,6 +107,26 @@ def run_trackedit(
         remove_red_flags_at_edge_threshold=remove_red_flags_at_edge_threshold,
     )
 
+    # Load InstanSeg model if enabled
+    instanseg_inference = None
+    if flag_allow_adding_instanseg_cell:
+        if instanseg_model_path is None:
+            raise ValueError(
+                "flag_allow_adding_instanseg_cell=True requires instanseg_model_path"
+            )
+
+        import torch
+
+        from trackedit.instanseg_inference import InstanSegInference
+
+        device = instanseg_device or ("cuda" if torch.cuda.is_available() else "cpu")
+
+        instanseg_inference = InstanSegInference(
+            model_path=instanseg_model_path,
+            device=device,
+            cache_size=1,  # Reduce memory usage - only cache current frame
+        )
+
     # overwrite some motile functions
     DeleteNodes._apply = create_db_delete_nodes(DB_handler)
     DeleteEdges._apply = create_db_delete_edges(DB_handler)
@@ -114,6 +142,8 @@ def run_trackedit(
         flag_show_hierarchy=flag_show_hierarchy,
         flag_allow_adding_spherical_cell=flag_allow_adding_spherical_cell,
         adding_spherical_cell_radius=adding_spherical_cell_radius,
+        flag_allow_adding_instanseg_cell=flag_allow_adding_instanseg_cell,
+        instanseg_inference=instanseg_inference,
     )
     if DB_handler.ndim == 4:
         viewer.dims.ndisplay = 3  # 3D view
diff --git a/trackedit/utils/red_flag_funcs.py b/trackedit/utils/red_flag_funcs.py
index 21e38c8..aa7a341 100644
--- a/trackedit/utils/red_flag_funcs.py
+++ b/trackedit/utils/red_flag_funcs.py
@@ -307,7 +307,8 @@ def filter_red_flags_at_edge(
 
     # Keep only 'added'/'removed' red flags that are NOT at the edge
     at_edge_mask = min_distance_to_edge <= edge_threshold
-    filtered_non_overlap = non_overlap_red_flags[~at_edge_mask]
+    # Use .values to avoid index alignment issues between non_overlap_red_flags and at_edge_mask
+    filtered_non_overlap = non_overlap_red_flags[~at_edge_mask.values]
 
     # Combine filtered non-overlap events with all overlap events
     filtered_red_flags = pd.concat(
diff --git a/trackedit/widgets/CustomEditingWidget.py b/trackedit/widgets/CustomEditingWidget.py
index 95017f3..04d7018 100644
--- a/trackedit/widgets/CustomEditingWidget.py
+++ b/trackedit/widgets/CustomEditingWidget.py
@@ -12,6 +12,7 @@ class CustomEditingMenu(EditingMenu):
     add_cell_button_pressed = Signal(int)
     duplicate_cell_button_pressed = Signal(int, int)
     add_spherical_cell_toggled = Signal(bool)  # Signal for spherical cell mode toggle
+    add_instanseg_cell_toggled = Signal(bool)  # Signal for InstanSeg cell mode toggle
 
     def __init__(
         self,
@@ -19,11 +20,13 @@ def __init__(
         databasehandler: DatabaseHandler,
         allow_adding_spherical_cell: bool = False,
         adding_spherical_cell_radius: int = 10,
+        allow_adding_instanseg_cell: bool = False,
     ):
         super().__init__(viewer)  # Call the original init method
         self.databasehandler = databasehandler
         self.allow_adding_spherical_cell = allow_adding_spherical_cell
         self.adding_spherical_cell_radius = adding_spherical_cell_radius
+        self.allow_adding_instanseg_cell = allow_adding_instanseg_cell
 
         main_layout = self.layout()  # This retrieves the QVBoxLayout from EditingMenu
         main_layout.insertWidget(0, QLabel(r"""<h3>Edit tracks</h3>"""))
@@ -82,11 +85,34 @@ def __init__(
             spherical_cell_layout.addWidget(self.add_spherical_cell_btn)
 
             node_box.layout().addLayout(spherical_cell_layout)
-            node_box.setMaximumHeight(200)  # Increased to fit spherical cell button
-            self.setMaximumHeight(480)  # Increased to fit spherical cell button
-        else:
+
+        # Conditionally add InstanSeg cell button
+        if self.allow_adding_instanseg_cell:
+            self.add_instanseg_cell_btn = QPushButton("Add InstanSeg Cell")
+            self.add_instanseg_cell_btn.setCheckable(True)  # Toggle on/off
+            self.add_instanseg_cell_btn.setStyleSheet(
+                "QPushButton:checked { background-color: #FF5722; color: white; }"
+            )
+            self.add_instanseg_cell_btn.clicked.connect(self._on_instanseg_cell_clicked)
+
+            instanseg_cell_layout = QHBoxLayout()
+            instanseg_cell_layout.addWidget(self.add_instanseg_cell_btn)
+
+            node_box.layout().addLayout(instanseg_cell_layout)
+
+        # Adjust heights based on which buttons are present
+        num_extra_buttons = sum(
+            [self.allow_adding_spherical_cell, self.allow_adding_instanseg_cell]
+        )
+        if num_extra_buttons == 0:
             node_box.setMaximumHeight(150)  # Original height
             self.setMaximumHeight(430)  # Original height
+        elif num_extra_buttons == 1:
+            node_box.setMaximumHeight(200)  # One extra button
+            self.setMaximumHeight(480)  # One extra button
+        else:  # num_extra_buttons == 2
+            node_box.setMaximumHeight(250)  # Two extra buttons
+            self.setMaximumHeight(530)  # Two extra buttons
 
     def update_add_cell_btn_state(self, text):
         state, _, _ = self.add_cell_input.validator().validate(text, 0)
@@ -123,3 +149,7 @@ def click_on_hierarchy_cell(self, label: int):
     def _on_spherical_cell_clicked(self, checked):
         """Emit signal when spherical cell button is toggled."""
         self.add_spherical_cell_toggled.emit(checked)
+
+    def _on_instanseg_cell_clicked(self, checked):
+        """Emit signal when InstanSeg cell button is toggled."""
+        self.add_instanseg_cell_toggled.emit(checked)

From 693f54f4ebfa52995d7277f26d1f30f5a40ebf80 Mon Sep 17 00:00:00 2001
From: TeunHuijben <teun.huijben@czbiohub.org>
Date: Fri, 13 Feb 2026 16:50:42 -0800
Subject: [PATCH 06/15] added red flags for trajectory and area changes

---
 scripts/script_instanseg.py       |   4 +-
 trackedit/DatabaseHandler.py      |  13 ++-
 trackedit/utils/red_flag_funcs.py | 152 ++++++++++++++++++++++++++++++
 3 files changed, 167 insertions(+), 2 deletions(-)

diff --git a/scripts/script_instanseg.py b/scripts/script_instanseg.py
index 0e01e14..abfab2f 100644
--- a/scripts/script_instanseg.py
+++ b/scripts/script_instanseg.py
@@ -13,7 +13,9 @@
 
 # **********INPUTS*********
 # path to the working directory that contains the database file AND metadata.toml:
-working_directory = Path("/home/teun.huijben/Documents/data/Thibaut/masks_on_geff/")
+working_directory = Path(
+    "/hpc/projects/group.royer/people/teun.huijben/data/Thibault/4th_exp//masks_on_geff/"
+)
 # name of the database file to start from, or "latest" to start from the latest version, defaults to "data.db"
 db_filename_start = "latest"
 # maximum number of frames display, defaults to None (use all frames)
diff --git a/trackedit/DatabaseHandler.py b/trackedit/DatabaseHandler.py
index b3dbcb7..71e9cce 100644
--- a/trackedit/DatabaseHandler.py
+++ b/trackedit/DatabaseHandler.py
@@ -28,7 +28,9 @@
     combine_red_flags,
     filter_red_flags_at_edge,
     find_all_starts_and_ends,
+    find_area_changes,
     find_overlapping_cells,
+    find_trajectory_changes,
 )
 from trackedit.utils.utils import (
     annotations_to_zarr,
@@ -605,6 +607,7 @@ def db_to_df(
                 NodeDB.z,
                 NodeDB.y,
                 NodeDB.x,
+                NodeDB.area,
                 NodeDB.generic,
             ),
         )
@@ -714,8 +717,16 @@ def find_all_red_flags(self) -> pd.DataFrame:
             df, self.db_path_new
         )
 
+        # Trajectory changes (jumps and direction changes)
+        rfs_trajectory = find_trajectory_changes(df, self.scale)
+
+        # Area/volume changes
+        rfs_area = find_area_changes(df)
+
         # Combine all red flag detection results
-        result_df = combine_red_flags(rfs_starts_and_ends, rfs_overlap)
+        result_df = combine_red_flags(
+            rfs_starts_and_ends, rfs_overlap, rfs_trajectory, rfs_area
+        )
 
         # ToDo: make option to filter redflags in the first two timepoints
         # (useful for neuromast with suboptimal beginning)
diff --git a/trackedit/utils/red_flag_funcs.py b/trackedit/utils/red_flag_funcs.py
index aa7a341..4dca677 100644
--- a/trackedit/utils/red_flag_funcs.py
+++ b/trackedit/utils/red_flag_funcs.py
@@ -1,3 +1,4 @@
+import numpy as np
 import pandas as pd
 import sqlalchemy as sqla
 from ultrack.core.database import OverlapDB, Session
@@ -322,3 +323,154 @@ def filter_red_flags_at_edge(
         )
 
     return filtered_red_flags
+
+
+def find_trajectory_changes(
+    df: pd.DataFrame,
+    scale: tuple,
+    displacement_threshold_multiplier: float = 2.5,
+    angle_threshold_deg: float = 120,
+    min_displacement_for_angle: float = None,
+) -> pd.DataFrame:
+    """
+    Detect trajectory jumps and direction changes.
+
+    Binary classification: 'good' or 'change' for each movement.
+    A movement is 'change' if EITHER:
+    - Large displacement (> threshold), OR
+    - Sharp direction change (> angle_threshold) AND displacement is significant
+
+    Parameters
+    ----------
+    df : pd.DataFrame
+        DataFrame with columns: track_id, t, z, y, x (indexed by id)
+    scale : tuple
+        (z_scale, y_scale, x_scale) for anisotropic scaling
+    displacement_threshold_multiplier : float
+        Multiplier for displacement threshold (default 2.5× the 95th percentile)
+    angle_threshold_deg : float
+        Angle change threshold in degrees (default 120)
+    min_displacement_for_angle : float or None
+        Min displacement to check angles (default: 75th percentile)
+
+    Returns
+    -------
+    pd.DataFrame
+        Red flags with columns: t, id, event='trajectory_change'
+    """
+
+    # Pass 1: Calculate all displacements for adaptive thresholds
+    all_displacements = []
+    for track_id, track_df in df.groupby("track_id"):
+        if len(track_df) < 2:
+            continue
+        track_df = track_df.sort_values("t")
+        dz = np.diff(track_df["z"].values) * scale[0]
+        dy = np.diff(track_df["y"].values) * scale[1]
+        dx = np.diff(track_df["x"].values) * scale[2]
+        displacements = np.sqrt(dz**2 + dy**2 + dx**2)
+        all_displacements.extend(displacements)
+
+    if len(all_displacements) == 0:
+        return pd.DataFrame(columns=["t", "id", "event"])
+
+    # Set thresholds
+    disp_threshold = (
+        np.percentile(all_displacements, 95) * displacement_threshold_multiplier
+    )
+    if min_displacement_for_angle is None:
+        min_displacement_for_angle = np.percentile(all_displacements, 75)
+
+    # Pass 2: Classify all movements
+    changes = []
+    for track_id, track_df in df.groupby("track_id"):
+        if len(track_df) < 2:
+            continue
+
+        track_df = track_df.sort_values("t")
+        z = track_df["z"].values
+        y = track_df["y"].values
+        x = track_df["x"].values
+        t = track_df["t"].values
+        ids = track_df.index.values
+
+        dz = np.diff(z) * scale[0]
+        dy = np.diff(y) * scale[1]
+        dx = np.diff(x) * scale[2]
+        displacements = np.sqrt(dz**2 + dy**2 + dx**2)
+
+        for i, disp in enumerate(displacements):
+            is_change = False
+
+            # Check 1: Large displacement?
+            if disp > disp_threshold:
+                is_change = True
+
+            # Check 2: Sharp direction change (only if displacement is significant)?
+            if disp > min_displacement_for_angle and i > 0:
+                v1 = np.array([dz[i - 1], dy[i - 1], dx[i - 1]])
+                v2 = np.array([dz[i], dy[i], dx[i]])
+                norm1 = np.linalg.norm(v1)
+                norm2 = np.linalg.norm(v2)
+
+                if norm1 > 0 and norm2 > 0:
+                    cos_angle = np.dot(v1, v2) / (norm1 * norm2)
+                    cos_angle = np.clip(cos_angle, -1, 1)
+                    angle_rad = np.arccos(cos_angle)
+                    angle_deg = np.degrees(angle_rad)
+
+                    if angle_deg > angle_threshold_deg:
+                        is_change = True
+
+            if is_change:
+                changes.append(
+                    {"t": t[i + 1], "id": ids[i + 1], "event": "trajectory_change"}
+                )
+
+    return pd.DataFrame(changes)
+
+
+def find_area_changes(df: pd.DataFrame, threshold: float = 0.5) -> pd.DataFrame:
+    """
+    Detect drastic area/volume changes.
+
+    Flags cells with >50% (default) area increase or decrease between timesteps.
+
+    Parameters
+    ----------
+    df : pd.DataFrame
+        DataFrame with columns: track_id, t, area (indexed by id)
+    threshold : float
+        Relative change threshold (default 0.5 = 50%)
+
+    Returns
+    -------
+    pd.DataFrame
+        Red flags with columns: t, id, event='area_change'
+    """
+
+    if "area" not in df.columns:
+        return pd.DataFrame(columns=["t", "id", "event"])
+
+    changes = []
+
+    for track_id, track_df in df.groupby("track_id"):
+        if len(track_df) < 2:
+            continue
+
+        track_df = track_df.sort_values("t")
+        areas = track_df["area"].values
+        t = track_df["t"].values
+        ids = track_df.index.values
+
+        # Relative change: (new - old) / old
+        relative_changes = np.diff(areas) / areas[:-1]
+
+        for i, rel_change in enumerate(relative_changes):
+            # Flag if absolute change > threshold (50% by default)
+            if np.abs(rel_change) > threshold:
+                changes.append(
+                    {"t": t[i + 1], "id": ids[i + 1], "event": "area_change"}
+                )
+
+    return pd.DataFrame(changes)

From 40f59e2d860538cb632681d3cd9b66d9047de955 Mon Sep 17 00:00:00 2001
From: TeunHuijben <teun.huijben@czbiohub.org>
Date: Wed, 18 Feb 2026 12:51:55 -0800
Subject: [PATCH 07/15] scale the edge_threshold when filtering red flags

---
 trackedit/DatabaseHandler.py      |  1 +
 trackedit/utils/red_flag_funcs.py | 54 +++++++++++++++----------------
 2 files changed, 27 insertions(+), 28 deletions(-)

diff --git a/trackedit/DatabaseHandler.py b/trackedit/DatabaseHandler.py
index 71e9cce..5dcea8e 100644
--- a/trackedit/DatabaseHandler.py
+++ b/trackedit/DatabaseHandler.py
@@ -740,6 +740,7 @@ def find_all_red_flags(self) -> pd.DataFrame:
                 data_shape=self.data_shape_full[1:],
                 edge_threshold=self.remove_red_flags_at_edge_threshold,
                 ndim=self.ndim,
+                scale=self.scale,
             )
 
         return result_df
diff --git a/trackedit/utils/red_flag_funcs.py b/trackedit/utils/red_flag_funcs.py
index 4dca677..4fd989c 100644
--- a/trackedit/utils/red_flag_funcs.py
+++ b/trackedit/utils/red_flag_funcs.py
@@ -211,6 +211,7 @@ def filter_red_flags_at_edge(
     data_shape: tuple,
     edge_threshold: int,
     ndim: int,
+    scale: tuple = None,
 ) -> pd.DataFrame:
     """
     Filter out 'added' and 'removed' red flags near the edge of the field of view (FOV).
@@ -232,10 +233,14 @@ def filter_red_flags_at_edge(
         - For 3D data: (z_max, y_max, x_max)
         - For 2D data: (y_max, x_max)
     edge_threshold : int
-        Distance threshold in pixels - cells within this distance from any
-        edge are considered "at edge"
+        Distance threshold in XY pixels. The Z threshold is derived by converting
+        this to a physical distance and back: z_threshold = edge_threshold * xy_scale / z_scale
     ndim : int
         Number of dimensions: 4 for 3D+time, 3 for 2D+time
+    scale : tuple or None
+        (z_scale, y_scale, x_scale) for 3D or (y_scale, x_scale) for 2D.
+        Used to make the Z threshold equivalent in physical units to the XY threshold.
+        If None, the same pixel threshold is used for all dimensions.
 
     Returns
     -------
@@ -275,39 +280,32 @@ def filter_red_flags_at_edge(
         df_full[["id", "z", "y", "x"]], on="id", how="left"
     )
 
-    # Calculate distance to edges for each red flag
+    # Compute per-dimension pixel thresholds
+    xy_threshold = edge_threshold
+    if ndim == 4 and scale is not None:
+        z_scale, y_scale, x_scale = scale
+        # Convert xy_threshold to physical distance, then to z pixels
+        z_threshold = edge_threshold * y_scale / z_scale
+    else:
+        z_threshold = edge_threshold  # Fall back to same threshold if no scale given
+
+    # Check each dimension against its own threshold and flag cells at any edge
+    pos = red_flags_with_pos
     if ndim == 4:
-        # 3D data: check z, y, x
         z_max, y_max, x_max = data_shape
-
-        distances_to_edges = pd.DataFrame(
-            {
-                "z_min": red_flags_with_pos["z"],
-                "z_max": z_max - red_flags_with_pos["z"] - 1,
-                "y_min": red_flags_with_pos["y"],
-                "y_max": y_max - red_flags_with_pos["y"] - 1,
-                "x_min": red_flags_with_pos["x"],
-                "x_max": x_max - red_flags_with_pos["x"] - 1,
-            }
+        at_edge_mask = (
+            (pos["z"] <= z_threshold) | (z_max - pos["z"] - 1 <= z_threshold) |
+            (pos["y"] <= xy_threshold) | (y_max - pos["y"] - 1 <= xy_threshold) |
+            (pos["x"] <= xy_threshold) | (x_max - pos["x"] - 1 <= xy_threshold)
         )
     else:
-        # 2D data: check y, x only
         y_max, x_max = data_shape
-
-        distances_to_edges = pd.DataFrame(
-            {
-                "y_min": red_flags_with_pos["y"],
-                "y_max": y_max - red_flags_with_pos["y"] - 1,
-                "x_min": red_flags_with_pos["x"],
-                "x_max": x_max - red_flags_with_pos["x"] - 1,
-            }
+        at_edge_mask = (
+            (pos["y"] <= xy_threshold) | (y_max - pos["y"] - 1 <= xy_threshold) |
+            (pos["x"] <= xy_threshold) | (x_max - pos["x"] - 1 <= xy_threshold)
         )
 
-    # Find minimum distance to any edge for each red flag
-    min_distance_to_edge = distances_to_edges.min(axis=1)
-
-    # Keep only 'added'/'removed' red flags that are NOT at the edge
-    at_edge_mask = min_distance_to_edge <= edge_threshold
+    # Keep only red flags that are NOT at the edge
     # Use .values to avoid index alignment issues between non_overlap_red_flags and at_edge_mask
     filtered_non_overlap = non_overlap_red_flags[~at_edge_mask.values]
 

From 826e73337924aae8aeab238c5fb924ce96608c01 Mon Sep 17 00:00:00 2001
From: TeunHuijben <teun.huijben@czbiohub.org>
Date: Wed, 18 Feb 2026 12:52:34 -0800
Subject: [PATCH 08/15] precommit

---
 trackedit/utils/red_flag_funcs.py | 15 ++++++++++-----
 1 file changed, 10 insertions(+), 5 deletions(-)

diff --git a/trackedit/utils/red_flag_funcs.py b/trackedit/utils/red_flag_funcs.py
index 4fd989c..df9830f 100644
--- a/trackedit/utils/red_flag_funcs.py
+++ b/trackedit/utils/red_flag_funcs.py
@@ -294,15 +294,20 @@ def filter_red_flags_at_edge(
     if ndim == 4:
         z_max, y_max, x_max = data_shape
         at_edge_mask = (
-            (pos["z"] <= z_threshold) | (z_max - pos["z"] - 1 <= z_threshold) |
-            (pos["y"] <= xy_threshold) | (y_max - pos["y"] - 1 <= xy_threshold) |
-            (pos["x"] <= xy_threshold) | (x_max - pos["x"] - 1 <= xy_threshold)
+            (pos["z"] <= z_threshold)
+            | (z_max - pos["z"] - 1 <= z_threshold)
+            | (pos["y"] <= xy_threshold)
+            | (y_max - pos["y"] - 1 <= xy_threshold)
+            | (pos["x"] <= xy_threshold)
+            | (x_max - pos["x"] - 1 <= xy_threshold)
         )
     else:
         y_max, x_max = data_shape
         at_edge_mask = (
-            (pos["y"] <= xy_threshold) | (y_max - pos["y"] - 1 <= xy_threshold) |
-            (pos["x"] <= xy_threshold) | (x_max - pos["x"] - 1 <= xy_threshold)
+            (pos["y"] <= xy_threshold)
+            | (y_max - pos["y"] - 1 <= xy_threshold)
+            | (pos["x"] <= xy_threshold)
+            | (x_max - pos["x"] - 1 <= xy_threshold)
         )
 
     # Keep only red flags that are NOT at the edge

From c1100771abb45f404b2566968004e5da2216a5c3 Mon Sep 17 00:00:00 2001
From: TeunHuijben <teun.huijben@czbiohub.org>
Date: Wed, 18 Feb 2026 14:33:52 -0800
Subject: [PATCH 09/15] script to mass-convert geff to db for challenge

---
 scripts/convert_geff_to_db.py | 95 +++++++++++++++++++++++++++++++++++
 trackedit/cli.py              |  2 +-
 trackedit/utils/geff.py       |  4 +-
 3 files changed, 98 insertions(+), 3 deletions(-)
 create mode 100644 scripts/convert_geff_to_db.py

diff --git a/scripts/convert_geff_to_db.py b/scripts/convert_geff_to_db.py
new file mode 100644
index 0000000..455d819
--- /dev/null
+++ b/scripts/convert_geff_to_db.py
@@ -0,0 +1,95 @@
+"""
+Script to convert GEFF files to Ultrack SQLite databases for the cellmotv1 tracking challenge.
+
+For each experiment folder:
+  - Runs: trackedit convert geff-to-db <experiment>.geff -o data.db
+  - Writes: metadata.toml with shape = [ 100, 64, 256, 256,]
+
+Usage:
+  python convert_geff_to_db.py              # test mode: processes one experiment, output to Desktop
+  python convert_geff_to_db.py --all        # processes all experiments, output next to geff/zarr
+"""
+
+import subprocess
+import sys
+from pathlib import Path
+
+BASE_DIR = Path(
+    "/hpc/projects/group.royer/people/thibaut.goldsborough/tracking-challenge/cellmotv1"
+)
+
+# Used only in test mode (no write access to BASE_DIR)
+TEST_OUTPUT_DIR = Path("/home/teun.huijben/Desktop/tracking-challenge/cellmotv1")
+
+SHAPE = [100, 64, 256, 256]
+
+METADATA_CONTENT = f"shape = {SHAPE}\n"
+
+
+def process_experiment(experiment_dir: Path, out_dir: Path) -> bool:
+    geff_files = list(experiment_dir.glob("*.geff"))
+    if not geff_files:
+        print(f"  [SKIP] No .geff file found in {experiment_dir}")
+        return False
+
+    geff_path = geff_files[0]
+    out_dir.mkdir(parents=True, exist_ok=True)
+    db_output = out_dir / "data.db"
+    metadata_path = out_dir / "metadata.toml"
+
+    print(f"  Converting: {geff_path.name}")
+    result = subprocess.run(
+        ["trackedit", "convert", "geff-to-db", str(geff_path), "-o", str(db_output)],
+        capture_output=True,
+        text=True,
+    )
+
+    if result.returncode != 0:
+        print(f"  [ERROR] Conversion failed:\n{result.stderr}")
+        return False
+
+    print(f"  Written:    {db_output}")
+
+    metadata_path.write_text(METADATA_CONTENT)
+    print(f"  Written:    {metadata_path}")
+
+    return True
+
+
+def main():
+    run_all = "--all" in sys.argv
+
+    date_folders = sorted(BASE_DIR.iterdir())
+
+    if run_all:
+        experiments = [
+            exp
+            for date_folder in date_folders
+            for exp in sorted(date_folder.iterdir())
+            if exp.is_dir()
+        ]
+        print(f"Processing all {len(experiments)} experiments...\n")
+        pairs = [(exp, exp) for exp in experiments]
+    else:
+        # Test mode: first experiment of the first folder, write to Desktop
+        first_folder = date_folders[0]
+        exp = sorted(first_folder.iterdir())[0]
+        experiments = [exp]
+        relative = exp.relative_to(BASE_DIR)
+        out = TEST_OUTPUT_DIR / relative
+        pairs = [(exp, out)]
+        print("TEST MODE: processing 1 experiment")
+        print(f"  Input:  {exp}")
+        print(f"  Output: {out}\n")
+
+    success = 0
+    for exp, out in pairs:
+        print(f"[{exp.parent.name}/{exp.name}]")
+        if process_experiment(exp, out):
+            success += 1
+
+    print(f"\nDone: {success}/{len(experiments)} experiments converted successfully.")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/trackedit/cli.py b/trackedit/cli.py
index 080e77c..e8131b5 100644
--- a/trackedit/cli.py
+++ b/trackedit/cli.py
@@ -27,7 +27,7 @@ def convert():
     help="Output database path (default: <input_stem>_from_geff.db)",
 )
 def geff_to_db(geff_path: Path, output: Path = None):
-    """Convert GEFF file to ULTrack SQLite database.
+    """Convert GEFF file to Ultrack SQLite database.
 
     Args:
         geff_path: Path to the input GEFF file
diff --git a/trackedit/utils/geff.py b/trackedit/utils/geff.py
index c485964..d24d4f5 100644
--- a/trackedit/utils/geff.py
+++ b/trackedit/utils/geff.py
@@ -14,7 +14,7 @@
 
 
 def convert_geff_to_db(geff_path: Path, output_path: Path = None) -> None:
-    """Convert GEFF file to ULTrack SQLite database.
+    """Convert GEFF file to Ultrack SQLite database.
 
     Args:
         geff_path: Path to the input GEFF file
@@ -226,7 +226,7 @@ def convert_geff_to_db(geff_path: Path, output_path: Path = None) -> None:
     help="Output database path (default: <input_stem>_to_db.db)",
 )
 def convert_geff_to_db_cli(geff_path: Path, output: Path = None) -> None:
-    """Convert GEFF file to ULTrack SQLite database (CLI version).
+    """Convert GEFF file to Ultrack SQLite database (CLI version).
 
     Args:
         geff_path: Path to the input GEFF file

From 81b888cba0cd9401565da65de4ecdcc8db89db11 Mon Sep 17 00:00:00 2001
From: TeunHuijben <teun.huijben@czbiohub.org>
Date: Fri, 27 Feb 2026 09:55:41 -0800
Subject: [PATCH 10/15] pixi lock linux

---
 pixi.lock | 6478 ++++++++++++++++++++++++-----------------------------
 1 file changed, 2887 insertions(+), 3591 deletions(-)

diff --git a/pixi.lock b/pixi.lock
index f56979a..5f1527a 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -12,20 +12,9 @@ environments:
       linux-64:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_gnu.tar.bz2
-      - conda: https://conda.anaconda.org/conda-forge/noarch/aiohappyeyeballs-2.6.1-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aiohttp-3.12.14-py311h3778330_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/aiosignal-1.4.0-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/alabaster-1.0.0-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/alsa-lib-1.2.14-hb9d3cd8_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/ampl-asl-1.0.0-h5888daf_2.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/annotated-types-0.7.0-pyhd8ed1ab_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aom-3.9.1-hac33072_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/app-model-0.3.0-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/appdirs-1.4.4-pyhd8ed1ab_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/asciitree-0.3.3-py_2.tar.bz2
-      - conda: https://conda.anaconda.org/conda-forge/noarch/asttokens-3.0.0-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/attr-2.5.1-h166bdaf_1.tar.bz2
-      - conda: https://conda.anaconda.org/conda-forge/noarch/attrs-25.3.0-pyh71513ae_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-auth-0.7.22-h96bc93b_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-cal-0.6.14-h88a6e22_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-common-0.9.19-h4ab18f5_0.conda
@@ -39,49 +28,28 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-checksums-0.1.18-h83b837d_6.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-crt-cpp-0.26.9-he3a8b3b_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-sdk-cpp-1.11.329-hba8bd5f_3.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/babel-2.17.0-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/blosc-1.21.5-hc2324a3_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/bokeh-3.7.3-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-1.1.0-hb9d3cd8_3.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-bin-1.1.0-hb9d3cd8_3.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-python-1.1.0-py311hfdbb021_3.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/brunsli-0.1-h9c3ff4c_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-h4bc722e_7.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.34.5-hb9d3cd8_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/c-blosc2-2.14.4-hb4ffafa_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2025.7.14-hbd8a1cb_0.conda
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       - conda: https://conda.anaconda.org/conda-forge/linux-64/cairo-1.18.0-h3faef2a_0.conda
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-      - conda: https://conda.anaconda.org/conda-forge/linux-64/debugpy-1.8.15-py311hc665b79_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/decorator-5.2.1-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/deprecated-1.2.18-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/distlib-0.4.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/distributed-2025.7.0-pyhe01879c_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/docstring_parser-0.17.0-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/docutils-0.21.2-pyhd8ed1ab_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.3.0-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/executing-2.2.0-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/donfig-0.8.1.post1-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/expat-2.7.1-hecca717_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/fasteners-0.19-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/filelock-3.18.0-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/flexcache-0.3-pyhd8ed1ab_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/flexparser-0.4-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab24e00_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-inconsolata-3.000-h77eed37_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-source-code-pro-2.038-h77eed37_0.tar.bz2
@@ -90,66 +58,43 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/noarch/fonts-conda-ecosystem-1-0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-0.tar.bz2
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-      - conda: https://conda.anaconda.org/conda-forge/linux-64/frozenlist-1.7.0-py311h52bc045_0.conda
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From c29f33700b02abbe27e4a14064e24723aba7708c Mon Sep 17 00:00:00 2001
From: Teun Huijben <teun.huijben@czbiohub.org>
Date: Fri, 27 Feb 2026 10:04:10 -0800
Subject: [PATCH 11/15] upgrade ilpy + add pyscipopt + mac pixi lock

---
 pixi.lock      | 165 ++++++++++++++++---------------------------------
 pyproject.toml |   3 +-
 2 files changed, 55 insertions(+), 113 deletions(-)

diff --git a/pixi.lock b/pixi.lock
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       - pypi: https://files.pythonhosted.org/packages/a5/ee/6e6c6715129c929af2d95ddb2e9decf54c1beffe58f336911197aacc0448/qtawesome-1.4.0-py3-none-any.whl
@@ -4117,8 +4105,6 @@ packages:
   - libgcc >=14
   - python >=3.11,<3.12.0a0
   - python_abi 3.11.* *_cp311
-  arch: x86_64
-  platform: linux
   license: Apache-2.0
   license_family: Apache
   purls:
@@ -4134,8 +4120,6 @@ packages:
   - python >=3.11,<3.12.0a0
   - python >=3.11,<3.12.0a0 *_cpython
   - python_abi 3.11.* *_cp311
-  arch: arm64
-  platform: osx
   license: Apache-2.0
   license_family: Apache
   purls:
@@ -4239,22 +4223,6 @@ packages:
   purls: []
   size: 1352411
   timestamp: 1717008621458
-- conda: https://conda.anaconda.org/gurobi/linux-64/gurobi-11.0.3-py311_0.tar.bz2
-  sha256: ac41673f45fa177a0f2e10da6843b4c6c453ab924d7ae908669ddbf60a90e1cf
-  md5: c1c1b138af195cec8b20db01ad8ba1f2
-  depends:
-  - python >=3.11,<3.12.0a0
-  license: PROPRIETARY
-  size: 39786430
-  timestamp: 1720618747013
-- conda: https://conda.anaconda.org/gurobi/osx-arm64/gurobi-11.0.3-py311_0.tar.bz2
-  sha256: 4bcecbcc3e3326594304a0adaa55a95d5e654686782f7a37250457cf5c0c74d2
-  md5: 058627f2d9419bda712f3cef41cd8ff8
-  depends:
-  - python >=3.11,<3.12.0a0
-  license: PROPRIETARY
-  size: 33219240
-  timestamp: 1720623973819
 - pypi: https://files.pythonhosted.org/packages/15/15/f700f39f6d3e51e7d03371e9392e851ddbbeea90b09d8de99e3e0ba20b66/gurobipy-12.0.3-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl
   name: gurobipy
   version: 12.0.3
@@ -4437,61 +4405,18 @@ packages:
   - pytest>=8.3.2 ; extra == 'all'
   - flake8>=7.1.1 ; extra == 'all'
   requires_python: '>=3.8'
-- pypi: https://files.pythonhosted.org/packages/75/30/70acdaac855895c1ede886437c22de46d16ae2ca299286230f16873fb3fa/ilpy-0.5.1-py3-none-any.whl
-  name: ilpy
-  version: 0.5.1
-  sha256: bc3a35ed5e394341cdf9be0c352b3e3f5421e22819b55bcb51b883149e76db36
-  requires_dist:
-  - pyscipopt
-  - gurobipy ; extra == 'dev'
-  - ipython ; extra == 'dev'
-  - mypy ; extra == 'dev'
-  - numpy ; extra == 'dev'
-  - pre-commit ; extra == 'dev'
-  - pyscipopt ; extra == 'dev'
-  - pytest ; extra == 'dev'
-  - pytest-cov ; extra == 'dev'
-  - ruff ; extra == 'dev'
-  - numpy ; extra == 'docs'
-  - sphinx ; extra == 'docs'
-  - sphinx-autodoc-typehints ; extra == 'docs'
-  - sphinx-rtd-theme ; extra == 'docs'
-  - gurobipy ; extra == 'gurobi'
-  - pyscipopt ; extra == 'scip'
-  - gurobipy ; extra == 'test'
-  - numpy ; extra == 'test'
-  - pyscipopt ; extra == 'test'
-  - pytest ; extra == 'test'
-  - pytest-cov ; extra == 'test'
-  requires_python: '>=3.9'
-- conda: https://conda.anaconda.org/funkelab/linux-64/ilpy-0.4.0-py311he0d2013_0.conda
-  sha256: 6c307716fb895c2ca34e5b392256722b39680b50daf2647c287de8e171fd63a3
-  md5: 65b9fdab46cba44da84e4ee00b22a3b4
-  depends:
-  - gurobi >=10.0,<12.0
-  - libgcc-ng >=12
-  - libstdcxx-ng >=12
-  - python >=3.11,<3.12.0a0
-  - python_abi 3.11.* *_cp311
-  - scip >=9.0.0,<10.0a0
-  license: MIT
-  license_family: MIT
-  size: 187968
-  timestamp: 1711718907087
-- conda: https://conda.anaconda.org/funkelab/osx-arm64/ilpy-0.4.0-py311h5235155_0.conda
-  sha256: b767e967a002381717820e307d70cbbb534ca15f8d6ccd6340e5a35d19f2486d
-  md5: 7b06dc893c5ba718a0d76424696d9555
+- conda: https://conda.anaconda.org/conda-forge/noarch/ilpy-0.5.1-pyhd8ed1ab_0.conda
+  sha256: 3c3e327ca5f6944f4b19622ebada884f046f84786516162fa802f3d25eec1b71
+  md5: ea3625ce051e0509d65feb7bee779b2b
   depends:
-  - gurobi >=10.0,<12.0
-  - libcxx >=16
-  - python >=3.11,<3.12.0a0
-  - python >=3.11,<3.12.0a0 *_cpython
-  - python_abi 3.11.* *_cp311
-  - scip >=9.0.0,<10.0a0
+  - numpy
+  - python >=3.9
   license: MIT
   license_family: MIT
-  size: 164097
-  timestamp: 1711719235661
+  purls:
+  - pkg:pypi/ilpy?source=hash-mapping
+  size: 24569
+  timestamp: 1744799020565
 - pypi: https://files.pythonhosted.org/packages/02/e1/8a299b91a4abee7c299c0c6625f9c0985c623fd4b6b41b5a5fe92508bb18/imagecodecs-2026.1.14-cp311-abi3-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
   name: imagecodecs
   version: 2026.1.14
@@ -9076,20 +9001,36 @@ packages:
   requires_dist:
   - numpy>=1.22.0
   requires_python: '>=3.9'
-- pypi: https://files.pythonhosted.org/packages/36/3c/158d647974810307ec4bec143cfe6b8044d338a42d326b31ac0b4ca181b8/pyscipopt-6.1.0.tar.gz
-  name: pyscipopt
-  version: 6.1.0
-  sha256: 7a6b144fd3a7485a85ffa2e6eea71d8251f2ca8bbc84cb2b36d6bb08d1c17e17
-  requires_dist:
-  - numpy>=1.16.0
-  requires_python: '>=3.8'
-- pypi: https://files.pythonhosted.org/packages/db/b4/c0310823179cccbc4e9ee51be82e2eac28944b68c5cb5b81daeeff811283/pyscipopt-6.1.0-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
-  name: pyscipopt
-  version: 6.1.0
-  sha256: 9cbd6750c5c0ac5a5cc56cd950a376235b482a11911a282dbca6373cc0ee0c7b
-  requires_dist:
-  - numpy>=1.16.0
-  requires_python: '>=3.8'
+- conda: https://conda.anaconda.org/conda-forge/linux-64/pyscipopt-5.0.1-py311hb755f60_0.conda
+  sha256: 828dc3088a6bb719c5e6ed89f0b230c130615a36f398ba10439d85d817b68021
+  md5: d5962480eaa5ae2cf2ab3297442fd44e
+  depends:
+  - libgcc-ng >=12
+  - libstdcxx-ng >=12
+  - python >=3.11,<3.12.0a0
+  - python_abi 3.11.* *_cp311
+  - scip >=9.0.0,<10.0a0
+  license: MIT
+  license_family: MIT
+  purls:
+  - pkg:pypi/pyscipopt?source=hash-mapping
+  size: 793101
+  timestamp: 1712330861303
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/pyscipopt-5.0.1-py311h92babd0_0.conda
+  sha256: 4346f225d3e871e9a769fc6e2836474229042d88300f2375403a615ee96ba2ff
+  md5: f42376b9aa628584f064684800e16986
+  depends:
+  - libcxx >=16
+  - python >=3.11,<3.12.0a0
+  - python >=3.11,<3.12.0a0 *_cpython
+  - python_abi 3.11.* *_cp311
+  - scip >=9.0.0,<10.0a0
+  license: MIT
+  license_family: MIT
+  purls:
+  - pkg:pypi/pyscipopt?source=hash-mapping
+  size: 648094
+  timestamp: 1712331291510
 - conda: https://conda.anaconda.org/conda-forge/noarch/pysocks-1.7.1-pyha55dd90_7.conda
   sha256: ba3b032fa52709ce0d9fd388f63d330a026754587a2f461117cac9ab73d8d0d8
   md5: 461219d1a5bd61342293efa2c0c90eac
@@ -10426,7 +10367,7 @@ packages:
 - pypi: .
   name: trackedit
   version: 0.0.17
-  sha256: 976e5e4c1092527a74d6e7d3028c4e78a37fc3e75688f36136defec15406ae91
+  sha256: e3ea06828dd0f0f532cdef2d99705bb2ff0625d97c09c333513b1fcf0b5f0b4c
   requires_dist:
   - click>=8.0
   - geff[rx]
@@ -10436,7 +10377,7 @@ packages:
 - pypi: .
   name: trackedit
   version: 0.0.17
-  sha256: 976e5e4c1092527a74d6e7d3028c4e78a37fc3e75688f36136defec15406ae91
+  sha256: e3ea06828dd0f0f532cdef2d99705bb2ff0625d97c09c333513b1fcf0b5f0b4c
   requires_dist:
   - click>=8.0
   - geff[rx]
diff --git a/pyproject.toml b/pyproject.toml
index 706d577..24f4a55 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -88,7 +88,8 @@ motile_tracker = { path = "./motile_tracker"}
 higra = ">=0.6.12,<0.7"
 numba = { version = ">=0.57.0,<0.61", channel = "numba" }
 llvmlite = { version = ">=0.44.0,<0.45", channel = "numba" }
-ilpy = ">=0.4.0,<0.5"
+ilpy = ">=0.5.1,<0.6"
+pyscipopt = "*" # needed for ilpy on mac
 # napari-ome-zarr = ">=0.6.1,<0.7"
 zarr = ">=3.0.0,<4.0.0" # zarr v3 support, ultrack now compatible
 psycopg2-binary = ">=2.9.6"

From 70169a26538f0cdf8b96e5193bedd3747aafac53 Mon Sep 17 00:00:00 2001
From: TeunHuijben <teun.huijben@czbiohub.org>
Date: Fri, 27 Feb 2026 10:10:09 -0800
Subject: [PATCH 12/15] pixi.lock linux after merge main

---
 pixi.lock | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/pixi.lock b/pixi.lock
index 56a77b7..9984ec7 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -10366,7 +10366,7 @@ packages:
   requires_python: '>=3.7'
 - pypi: .
   name: trackedit
-  version: 0.0.17
+  version: 0.0.18
   sha256: e3ea06828dd0f0f532cdef2d99705bb2ff0625d97c09c333513b1fcf0b5f0b4c
   requires_dist:
   - click>=8.0
@@ -10376,7 +10376,7 @@ packages:
   editable: true
 - pypi: .
   name: trackedit
-  version: 0.0.17
+  version: 0.0.18
   sha256: e3ea06828dd0f0f532cdef2d99705bb2ff0625d97c09c333513b1fcf0b5f0b4c
   requires_dist:
   - click>=8.0

From 5db98c3a1e1bafb004083829c532d68dd8bd2f60 Mon Sep 17 00:00:00 2001
From: Teun Huijben <teun.huijben@czbiohub.org>
Date: Fri, 27 Feb 2026 10:13:36 -0800
Subject: [PATCH 13/15] pixi.lock mac after merge main

---
 pixi.lock | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pixi.lock b/pixi.lock
index 9984ec7..86f8981 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -10366,7 +10366,7 @@ packages:
   requires_python: '>=3.7'
 - pypi: .
   name: trackedit
-  version: 0.0.18
+  version: 0.0.17
   sha256: e3ea06828dd0f0f532cdef2d99705bb2ff0625d97c09c333513b1fcf0b5f0b4c
   requires_dist:
   - click>=8.0

From 4be0be0d0acee293a01211c606c3c3af97c54571 Mon Sep 17 00:00:00 2001
From: TeunHuijben <teun.huijben@czbiohub.org>
Date: Fri, 27 Feb 2026 10:14:12 -0800
Subject: [PATCH 14/15] pixi.lock linux again...

---
 pixi.lock | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pixi.lock b/pixi.lock
index 86f8981..9984ec7 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -10366,7 +10366,7 @@ packages:
   requires_python: '>=3.7'
 - pypi: .
   name: trackedit
-  version: 0.0.17
+  version: 0.0.18
   sha256: e3ea06828dd0f0f532cdef2d99705bb2ff0625d97c09c333513b1fcf0b5f0b4c
   requires_dist:
   - click>=8.0

From 3543a4e9b407fe1593748f39043bcdfed479e0fa Mon Sep 17 00:00:00 2001
From: TeunHuijben <teun.huijben@czbiohub.org>
Date: Fri, 27 Feb 2026 16:24:02 -0800
Subject: [PATCH 15/15] make instanseg part compatible with neuromast 2channel
 model

---
 scripts/script_neuromast.py      | 29 ++++++++---
 trackedit/DatabaseHandler.py     | 14 +++---
 trackedit/TrackEditClass.py      | 59 +++++++++++++---------
 trackedit/instanseg_inference.py | 86 +++++++++++++++++++++++---------
 4 files changed, 126 insertions(+), 62 deletions(-)

diff --git a/scripts/script_neuromast.py b/scripts/script_neuromast.py
index 51b41de..c4ee6da 100644
--- a/scripts/script_neuromast.py
+++ b/scripts/script_neuromast.py
@@ -14,24 +14,25 @@
 # **********INPUTS*********
 # path to the working directory that contains the database file AND metadata.toml:
 working_directory = Path(
-    "/hpc/projects/group.royer/people/teun.huijben/data/Akila/trackedit_example_data/"
+    "/hpc/projects/group.royer/people/teun.huijben/data/Akila/trackedit_example_data_v2/"
 )
 # name of the database file to start from, or "latest" to start from the latest version, defaults to "data.db"
 db_filename_start = "latest"
 # maximum number of frames display, defaults to None (use all frames)
 tmax = 600
 # (Z),Y,X, defaults to (1, 1, 1)
-scale = (2.31, 1, 1)
+scale = (0.25, 0.108, 0.108)  # microns
 # overwrite existing database/changelog, defaults to False (not used when db_filename_start is "latest")
 allow_overwrite = False
 
 # OPTIONAL: imaging data
-# imaging_zarr_file = (
-#     "/hpc/....../deconvolved.zarr"
-# )
-# imaging_channel = "0/4/0/0"
-imaging_zarr_file = None
-imaging_channel = None
+imaging_zarr_file = (
+    "/hpc/projects/group.royer/people/teun.huijben/data/Akila/"
+    "trackedit_example_data_v2/deconvolved_and_registered_corrected_cropped850.zarr"
+)
+imaging_channel = "0/3/0/0"
+# imaging_zarr_file = None
+# imaging_channel = None
 
 # OPTIONAL: annotation mapping (default is neuromast cell types)
 # annotation_mapping = {
@@ -40,6 +41,15 @@
 #     3: {"name": "mantle", "color": [0.0, 0.0, 0.9, 1.0]},  # blue
 # }
 annotation_mapping = None
+
+# OPTIONAL: InstanSeg model for interactive cell segmentation
+# Enable this to add cells via InstanSeg inference instead of spherical masks
+flag_allow_adding_instanseg_cell = True
+instanseg_model_path = (
+    "/hpc/projects/group.royer/people/teun.huijben/data/Thibault/"
+    "neuromast_model/instanseg_27527750.pt"
+)
+instanseg_device = None  # 'cuda', 'cpu', or None for auto-detect
 # *************************
 
 if __name__ == "__main__":
@@ -52,4 +62,7 @@
         imaging_zarr_file=imaging_zarr_file,
         imaging_channel=imaging_channel,
         annotation_mapping=annotation_mapping,
+        flag_allow_adding_instanseg_cell=flag_allow_adding_instanseg_cell,
+        instanseg_model_path=instanseg_model_path,
+        instanseg_device=instanseg_device,
     )
diff --git a/trackedit/DatabaseHandler.py b/trackedit/DatabaseHandler.py
index 5dcea8e..0aef118 100644
--- a/trackedit/DatabaseHandler.py
+++ b/trackedit/DatabaseHandler.py
@@ -28,9 +28,7 @@
     combine_red_flags,
     filter_red_flags_at_edge,
     find_all_starts_and_ends,
-    find_area_changes,
     find_overlapping_cells,
-    find_trajectory_changes,
 )
 from trackedit.utils.utils import (
     annotations_to_zarr,
@@ -718,15 +716,17 @@ def find_all_red_flags(self) -> pd.DataFrame:
         )
 
         # Trajectory changes (jumps and direction changes)
-        rfs_trajectory = find_trajectory_changes(df, self.scale)
+        # rfs_trajectory = find_trajectory_changes(df, self.scale)
 
         # Area/volume changes
-        rfs_area = find_area_changes(df)
+        # rfs_area = find_area_changes(df)
 
         # Combine all red flag detection results
-        result_df = combine_red_flags(
-            rfs_starts_and_ends, rfs_overlap, rfs_trajectory, rfs_area
-        )
+        # result_df = combine_red_flags(
+        # rfs_starts_and_ends, rfs_overlap, rfs_trajectory, rfs_area
+        # )
+
+        result_df = combine_red_flags(rfs_starts_and_ends, rfs_overlap)
 
         # ToDo: make option to filter redflags in the first two timepoints
         # (useful for neuromast with suboptimal beginning)
diff --git a/trackedit/TrackEditClass.py b/trackedit/TrackEditClass.py
index 3714373..43e6ba7 100644
--- a/trackedit/TrackEditClass.py
+++ b/trackedit/TrackEditClass.py
@@ -181,6 +181,13 @@ def add_tracks(self):
 
         # Add tracks and verify layer creation
         self.tracksviewer.tracks_list.add_tracks(tracks, name=self.databasehandler.name)
+
+        # Set point size to 0.5 µm. Positions are stored in micron units (scaled by
+        # z/y/x_scale), so size=5 (the motile_tracker default) is 5 µm — too large.
+        points_layer = self.tracksviewer.tracking_layers.points_layer
+        if points_layer is not None:
+            points_layer.set_point_size(0.5)
+
         self.viewer.layers.selection.active = self.viewer.layers[
             self.databasehandler.name + "_seg"
         ]  # select segmentation layer
@@ -664,18 +671,20 @@ def add_instanseg_cell_at_position(self, viewer, current_time):
             show_warning("InstanSeg inference engine not initialized.")
             return None
 
-        # Update chunk if needed
-        self.update_chunk_from_frame(current_time)
-
-        # Get image at current time - use sparse channel (index 1)
-        image_volume = self.databasehandler.imagingArray.get_channel_data(1)[
-            current_time
-        ]  # (Z, Y, X) or (Y, X)
-
-        # Convert dask array to numpy array if needed
-        if hasattr(image_volume, "compute"):
-            image_volume = image_volume.compute()
-        image_volume = np.asarray(image_volume)
+        # No chunk update needed: the click comes from the viewer cursor so we are
+        # already on the correct chunk. update_chunk_from_frame triggers add_tracks()
+        # which is expensive (~4-5s) and unnecessary here.
+
+        # Get all channels at current time and stack to (C, Z, Y, X) or (C, Y, X).
+        # Ray casting uses channel 0 (nuclear); all channels are fed to the model.
+        imaging = self.databasehandler.imagingArray
+        volumes = []
+        for ch_idx in range(imaging.n_channels):
+            ch_data = imaging.get_channel_data(ch_idx)[current_time]
+            if hasattr(ch_data, "compute"):
+                ch_data = ch_data.compute()
+            volumes.append(np.asarray(ch_data))
+        image_volume = np.stack(volumes, axis=0)  # (C, Z, Y, X) or (C, Y, X)
 
         # Get view direction for ray casting
         vd_world = getattr(viewer.cursor, "_view_direction", None)
@@ -717,8 +726,12 @@ def add_instanseg_cell_at_position(self, viewer, current_time):
 
         origin_data = cursor_spatial_world / scale_array
 
+        # image_volume is (C, Z, Y, X) or (C, Y, X); spatial shape excludes channel dim
+        spatial_shape = image_volume.shape[1:]
+        nuclear_volume = image_volume[0]  # use nuclear channel (idx 0) for ray casting
+
         # Cast ray through volume in both directions
-        diag = int(np.sqrt(sum(s**2 for s in image_volume.shape)))
+        diag = int(np.sqrt(sum(s**2 for s in spatial_shape)))
         t_values = np.arange(-diag, diag + 1, dtype=float)
         ray_points = origin_data[None, :] + t_values[:, None] * vd_data[None, :]
         ray_voxels = np.round(ray_points).astype(int)
@@ -727,18 +740,18 @@ def add_instanseg_cell_at_position(self, viewer, current_time):
         if self.databasehandler.ndim == 4:
             valid = (
                 (ray_voxels[:, 0] >= 0)
-                & (ray_voxels[:, 0] < image_volume.shape[0])
+                & (ray_voxels[:, 0] < spatial_shape[0])
                 & (ray_voxels[:, 1] >= 0)
-                & (ray_voxels[:, 1] < image_volume.shape[1])
+                & (ray_voxels[:, 1] < spatial_shape[1])
                 & (ray_voxels[:, 2] >= 0)
-                & (ray_voxels[:, 2] < image_volume.shape[2])
+                & (ray_voxels[:, 2] < spatial_shape[2])
             )
         else:
             valid = (
                 (ray_voxels[:, 0] >= 0)
-                & (ray_voxels[:, 0] < image_volume.shape[0])
+                & (ray_voxels[:, 0] < spatial_shape[0])
                 & (ray_voxels[:, 1] >= 0)
-                & (ray_voxels[:, 1] < image_volume.shape[1])
+                & (ray_voxels[:, 1] < spatial_shape[1])
             )
 
         ray_voxels = ray_voxels[valid]
@@ -746,14 +759,14 @@ def add_instanseg_cell_at_position(self, viewer, current_time):
             show_warning("Ray does not intersect volume. Try clicking inside the data.")
             return None
 
-        # Deduplicate and find voxel with maximum intensity (brightest pixel)
+        # Deduplicate and find voxel with maximum intensity in nuclear channel
         ray_voxels = np.unique(ray_voxels, axis=0)
         if self.databasehandler.ndim == 4:
-            intensities = image_volume[
+            intensities = nuclear_volume[
                 ray_voxels[:, 0], ray_voxels[:, 1], ray_voxels[:, 2]
             ]
         else:
-            intensities = image_volume[ray_voxels[:, 0], ray_voxels[:, 1]]
+            intensities = nuclear_volume[ray_voxels[:, 0], ray_voxels[:, 1]]
 
         best_idx = int(np.argmax(intensities))
         best_voxel = ray_voxels[best_idx]
@@ -768,8 +781,8 @@ def add_instanseg_cell_at_position(self, viewer, current_time):
                 self.databasehandler.x_scale,
             )
         else:
-            # 2D data: InstanSeg expects 3D, add singleton z dimension
-            image_volume = image_volume[np.newaxis, ...]  # (1, Y, X)
+            # 2D data: InstanSeg expects 3D, add singleton z dimension per channel
+            image_volume = image_volume[:, np.newaxis, ...]  # (C, 1, Y, X)
             position_data = (0,) + position_data  # (0, y, x)
             # Use y_scale as z_scale for isotropic rescaling
             scale = (
diff --git a/trackedit/instanseg_inference.py b/trackedit/instanseg_inference.py
index 6c0561a..41a3d13 100644
--- a/trackedit/instanseg_inference.py
+++ b/trackedit/instanseg_inference.py
@@ -14,7 +14,8 @@
 from napari.utils.notifications import show_warning
 
 # Target pixel size for isotropic rescaling (must match training)
-TARGET_PIXEL_SIZE = 0.5
+TARGET_PIXEL_SIZE = 0.3
+_BACKBONE_DIVISOR = 32  # UNet spatial dims must be divisible by this (2^depth)
 
 
 def normalize(image: np.ndarray) -> np.ndarray:
@@ -67,21 +68,53 @@ def rescale_to_isotropic(
 
 
 def rescale_labels_back(labels: np.ndarray, original_shape: tuple) -> np.ndarray:
-    """Rescale labels from isotropic back to original shape using nearest interpolation."""
+    """Rescale a 3D binary mask from isotropic back to original shape.
+
+    Uses trilinear interpolation (mode="trilinear" required for 5D tensors:
+    batch+channel+Z+Y+X). Thresholds at 0.5 to recover a binary mask with
+    smooth sub-voxel boundaries instead of nearest-neighbor staircase artefacts.
+    """
     tensor = torch.from_numpy(labels.astype(np.float32)).unsqueeze(0).unsqueeze(0)
-    rescaled = F.interpolate(tensor, size=original_shape, mode="nearest")
-    return rescaled.squeeze().numpy().astype(np.int32)
+    rescaled = F.interpolate(
+        tensor, size=original_shape, mode="trilinear", align_corners=False
+    )
+    return (rescaled.squeeze().numpy() > 0.5).astype(np.int32)
 
 
 def run_backbone(model, image_normalized: np.ndarray, device: str) -> torch.Tensor:
-    """Run only the backbone. Returns (C, D, H, W) prediction tensor on GPU."""
+    """Run only the backbone. Returns (C, D, H, W) prediction tensor on GPU.
+
+    Args:
+        image_normalized: (Z, Y, X) single-channel or (C, Z, Y, X) multi-channel
+    """
     tensor = torch.from_numpy(image_normalized).float()
     if tensor.ndim == 3:
         tensor = tensor.unsqueeze(0).unsqueeze(0)  # (1, 1, Z, Y, X)
+    elif tensor.ndim == 4:
+        tensor = tensor.unsqueeze(0)  # (1, C, Z, Y, X)
+
+    # UNet skip connections require spatial dims divisible by _BACKBONE_DIVISOR.
+    # Pad to the next multiple, run backbone, then crop output back.
+    original_spatial = tensor.shape[2:]  # (Z, Y, X)
+    padded_spatial = tuple(
+        ((s + _BACKBONE_DIVISOR - 1) // _BACKBONE_DIVISOR) * _BACKBONE_DIVISOR
+        for s in original_spatial
+    )
+    # F.pad takes padding in reverse dim order: (x_before, x_after, y_before, y_after, ...)
+    pad_args = []
+    for orig, padded in zip(reversed(original_spatial), reversed(padded_spatial)):
+        pad_args += [0, padded - orig]
+    tensor = F.pad(tensor, pad_args)
+
     tensor = tensor.to(device)
     with torch.no_grad():
-        pred = model.backbone(tensor)  # (1, C, D, H, W)
-    return pred[0]  # (C, D, H, W), stays on device
+        pred = model.backbone(tensor)  # (1, C, D_pad, H_pad, W_pad)
+
+    # Crop back to original spatial size before returning
+    pred_cropped = pred[
+        0, :, : original_spatial[0], : original_spatial[1], : original_spatial[2]
+    ]
+    return pred_cropped  # (C, D, H, W), stays on device
 
 
 def run_postprocessing(
@@ -245,7 +278,7 @@ def run_inference_at_position(
         """Run InstanSeg inference with seed at position.
 
         Args:
-            image_volume: Raw image volume (Z, Y, X) at original resolution
+            image_volume: Raw image volume (C, Z, Y, X) with all channels
             time_frame: Time frame index (for caching)
             position: (z, y, x) seed position in original data coordinates
             scale: (z_scale, y_scale, x_scale) voxel sizes in microns
@@ -253,18 +286,28 @@ def run_inference_at_position(
         Returns:
             (mask, bbox) tuple:
             - mask: Binary mask (Z, Y, X) at original resolution, or None if no cell
-            - bbox: Bounding box ((z_min, y_min, x_min), (z_max, y_max, x_max)), or None
+            - bbox: Bounding box as flat array [z_min, y_min, x_min, z_max, y_max, x_max], or None
         """
         try:
-            # Normalize image
-            image_normalized = normalize(image_volume)
-
-            # Rescale to isotropic resolution (on GPU if available)
-            image_isotropic, scale_factors = rescale_to_isotropic(
-                image_normalized, scale, TARGET_PIXEL_SIZE, device=self.device
-            )
-            iso_shape = image_isotropic.shape
-            original_shape = image_volume.shape
+            # image_volume is (C, Z, Y, X); extract spatial shape for coordinate math
+            n_channels = image_volume.shape[0]
+            original_shape = image_volume.shape[1:]  # (Z, Y, X)
+
+            # Rescale to isotropic first, then normalize on the smaller volume.
+            # e.g. 73×1024×1024 → 61×256×256 is ~19× fewer pixels for percentile.
+            iso_channels = []
+            scale_factors = None
+            for c in range(n_channels):
+                ch_isotropic, scale_factors = rescale_to_isotropic(
+                    image_volume[c].astype(np.float32),
+                    scale,
+                    TARGET_PIXEL_SIZE,
+                    device=self.device,
+                )
+                ch_normalized = normalize(ch_isotropic)
+                iso_channels.append(ch_normalized)
+            image_isotropic = np.stack(iso_channels, axis=0)  # (C, Z_iso, Y_iso, X_iso)
+            iso_shape = image_isotropic.shape[1:]  # spatial only, for coord conversion
 
             # Check cache for embeddings
             if time_frame in self.cached_embeddings:
@@ -289,7 +332,7 @@ def run_inference_at_position(
                     original_shape,
                 )
 
-            # Convert position to isotropic coordinates
+            # Convert position to isotropic coordinates (spatial shapes only)
             position_iso = self._convert_to_isotropic_coords(
                 position, original_shape, iso_shape
             )
@@ -373,11 +416,6 @@ def _compute_and_cache_embeddings(
         # Run backbone inference
         embeddings = run_backbone(self.model, image_isotropic, self.device)
 
-        # Report GPU memory usage if available
-        if self.device == "cuda" and torch.cuda.is_available():
-            gpu_mem_mb = torch.cuda.memory_allocated() / 1e6
-            print(f"GPU memory: {gpu_mem_mb:.0f} MB")
-
         # Add to cache
         self.cached_embeddings[time_frame] = (
             embeddings,