Enhanced Segmentation for CT Images of Visceral Organs Based on Directional Connectivity

This study investigates the application of the DconnNet model for the segmentation of CT images of visceral organs. Experimental results show that DconnNet significantly outperforms U-Net across various metrics, highlighting its su-perior capability in accurately delineating complex structures within medical CT images. The DconnNet model improves feature representation by effectively disentangling directional subspaces from the shared latent space, leading to more anatomically consistent segmentation. This method performs exceptionally in maintaining spatial coherence and robustness amidst noise and artifacts, which makes it highly suitable for clinical applications. Despite its higher computational demands, DconnNet's performance highlights its potential as a powerful tool for medical image segmentation. Further research can focus on reducing computational load, extending its application to other medical imaging modalities, and incorporating advanced learning techniques to further improve its generalizability and efficiency.