Comparison of Texture-based Classification and Deep Learning for Plantar Soft Tissue Histology Segmentation

Semantic segmentation of plantar soft tissue histology images using several machine learning techniques. Published in Computers in Biology and Medicine, 2021 Application of the trained Unet method is published in The Foot, 2023

Sup Deep Morphology

Texture-based Classification

• lbp folder: code to complete local binary patterns
• subroutines folder: all subroutines needed to run the main scripts
• perceptual: code to extract perceptual features
• SNIC_mex: Slightly adapted SNIC method.

Running instructions

• use main_create_texture_feature_dataset.m to extract desired features from all classifier images.
• use main_select_features.m to reduce the size of the feature set
• use main_train_classifier_reduced.m to train the classifier on the training data extracted in step 1
• use one of the main_deploy_classifier*.m to apply the trained classifier to the whole slide images using desired strategy (block or superpixel)

Deep Learning

• UNet7Channel is the caffe prototxt file describing the network architecture. Use netscope to visualize the architecture.
• MakeDeepLearningData.m file will take in images and batch crop or augment and save resulting files for input into deep neural network
• getRangAug.m is used to randomly augment the data; function called by Make*Data.m
• StitchDigitsOutput.m and AverageOverlap_Stitch.m are used to stitch the network output back into the original input size.
• caffe installation adapted from happynear

Data

Data can be found at UW research works. There should be a zipped file containing the following folders:

• GroundTruth contains raw images correlating to ground truth label matrices
• classifierims contains the folder of single-tissue images from which texture features were extracted.
• featureSets contains extracted feature sets used to train the classifiers
• trainedClassifiers contains the trained classifiers used for whole slide iamge segmentation
• UNet_models contains 3 checkpoints of the best version of the UNet. Checkpoint 8000 was used for final segmentation comparison

System info

• MATLAB code was run on windows and Liunux systems (Win 7, 10; Ubuntu 12)
• Python code for caffe run on Win 7.
• All other O.S. have not been tested

Licensing and copyright

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