This repository contains the methods described in the following articles:
Borne L., Rivière D., Mancip M. and Mangin J.F., 2020. Automatic labeling of cortical sulci using patch-or CNN-based segmentation techniques combined with bottom-up geometric constraints. Medical Image Analysis
This paper proposes and compares methods to automatically label the cortical folds. The code developed for the UNET model is available here.
If you want to appply the model on your own dataset, the trained model is usable in the latest of Morphologist in BrainVisa.
Borne L., Rivière D., Cachia A., Roca P., Mellerio C., Oppenheim C. and Mangin J.F., 2021. Automatic recognition of specific local cortical folding patterns. NeuroImage
The second paper proposes 3 methods to automatically classify local cortical folding patterns: the first one based on a Support Vector Machine (SVM) classifier, the second one based on Scoring by Non-local Image Patch Estimator (SNIPE) and the third one based on a convolutionnal neural networks (Resnet). The code developed for these 3 methods is available here.