DeepMorph-CC: a deep learning pipeline for accurate corpus callosum morphometric analysis

The corpus callosum (CC), the largest white matter structure in the human brain, plays a vital role in interhemispheric communication and is closely associated with several neurological disorders. Accurate morphometric analysis of the CC can provide important insights into disease-related structural alterations. However, manual segmentation of the CC from MRI scans is time-consuming and subject to interobserver variability, limiting its applicability in large-scale neuroimaging studies. To address these challenges, we present DeepMorph-CC, an automated deep learning framework for segmentation and quantitative morphometric analysis of the CC from 2D T1-weighted MRI images. Rather than introducing a new segmentation backbone, the proposed approach focuses on the task-specific adaptation of an attention-guided U-Net architecture and its integration into a unified analysis pipeline. Spatial attention mechanisms are employed to improve boundary localization and segmentation accuracy, whereas K-fold cross-validation has been utilized to enhance model robustness under limited training data conditions. Following segmentation, anatomically consistent CC parcellation has been performed to enable region-wise morphometric analysis. Key structural features, including area, circularity, solidity, and major and minor axis lengths, are extracted for the entire CC and its subregions. The proposed framework is evaluated using two publicly available datasets, the ABIDE dataset and the OASIS dataset, along with a real clinical MRI dataset (RCI). Experimental results achieve an average DSC of 98.70±0.37%, demonstrating high segmentation accuracy. Consistent morphometric patterns across datasets and strong agreement with expert annotations validate the effectiveness and reliability of the proposed framework. Future work will focus on extending the framework to multicenter datasets and three-dimensional MRI volumes