ACCP-MC-U-Net: Automatic Corpus Callosum Parcellation from brain MRI scans using MultiClass U-Net

Abstract

Accurate segmentation of the Corpus Callosum (CC) plays a crucial role in studying brain connectivity and understanding neurological disorders. However, limited availability of annotated data poses a significant challenge for developing robust segmentation models. In order to deal with this issue, we propose in the study an effective approach that combines one-shot learning and a modified multiclass U-Net architecture. The proposed approach represents the first attempt in this context, to the best of our knowledge. We begin by generating additional Ground Truth (GT) data using one-shot learning, effectively expanding the limited annotated dataset. This approach leverages the inherent generalization capability of one-shot learning to predict segmentation for unlabeled data, which are then validated and refined by domain experts. The refined segmentation serves as new GT data, enhancing the training process. To further improve parcellation accuracy, we modify the U-Net architecture to handle the complex task of multiclass CC parcellation. The modified multiclass U-Net effectively captures the intricate features and spatial dependencies within the CC, enabling precise parcellation into distinct sub-regions. The framework has been tested and evaluated on two challenging datasets that are publicly available. The obtained results are promising and show the performance of the proposed solution against geometric methods from the state of the art.