Medical image segmentation serves as a critical technology to link medical imaging data with clinical decision-making. However, existing segmentation methods still face significant challenges in boundary delineation: the spatial inconsistency caused by non-rigid deformations of anatomical structures induced by respiratory motion and organ shifts, and the loss of fine details during hierarchical feature extraction, leading to degraded edge precision.To address these issues, we propose a novel Multi-scale Connectivity-Edge Pixel-level Attention Network (MCEPANet). Specifically, we introduce a statistical pixel-level connectivity extraction algorithm that formally characterizes anatomical topology through spatial correlation analysis, improving the model's robustness against organ deformation and inter-subject variability. A Connectivity-Edge Pixel-level Attention (CEPA) module is proposed to adaptively integrate geometric edge features extracted from traditional edge detection technique with the learned pixel-level connectivity priors through channel-wise attention mechanisms. This design explicitly mitigates edge information degradation and significantly improves boundary representation capabilities. The MCEPANet incorporates the CEPA module at multiple network depths to capture edge details across different spatial scales, alleviating the boundary blurring problem prevalent in medical image segmentation tasks.Extensive experiment results on the Synapse multi-organ abdominal segmentation dataset demonstrate MCEPANet's superiority, achieving an average Dice Similarity Coefficient (DSC) of 84.15% and an average 95% Hausdorff Distance (HD95) of 14.88 mm, outperforming the existing state-of-the-art two dimensional segmentation methods. Cross-domain validation on the ACDC cardiac dataset confirms exceptional generalizability, highlighting its versatility for different clinical segmentation tasks.
Wu et al. (Thu,) studied this question.
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