knee-cartilage-segmentation

The project covers various deep learning and traditional machine learning models to automate the segmentation of knee cartilages using the diffusion weighted MRIs.

The best deep learning model performs at the human-expert level.

Below are the results for the different Deep Learning Models.

Model Name	Dice Score (Femur)	Dice Score (Patella)	Dice Score (Tibia)
Baseline U-Net Model	0.671	0.745	0.573
Dilated U-Net Model	0.681	0.764	0.580
Dilated U-Net (L2 Regularized)	0.683	0.751	0.552
Small U-Net	0.678	0.773	0.593
Small Dilated U-Net	0.670	0.771	0.621
Ensemble of Small U-Net and Small Dilated U-Net	0.689	0.783	0.640
Human Expert Re-segmentation Dice Score	0.711	0.743	0.629

Visualizartion of the predictions and the ground truth

Case where the model correctly predicted ground truth. Left: Original Image, Center: Ground Truth, Right: Model Prediction. Femur = Pink, Patella = Red, Tibia = Yellow
Case where the model correctly predicted a segment not present in the ground truth. Left: Original Image, Center: Ground Truth, Right: Model Prediction. Femur = Pink, Patella = Red, Tibia = Yellow
Case where the model was more conservative than the human expert. Left: Original Image, Center: Ground Truth, Right: Model Prediction. Femur = Pink, Patella = Red, Tibia = Yellow
Case where the human expert was more conservative than the model. Left: Original Image, Center: Ground Truth, Right: Model Prediction. Femur = Pink, Patella = Red, Tibia = Yellow

Confidence maps

Confidence maps are made that helps the user to understand how confident the model is of the predictions.

A confidence map for two example validation images can be seen below. The circled stray voxels are incorrectly classified as one of the cartilage. It can be seen that incorrect voxels have low confidence.