E-SegNet: E-Shaped Structure Networks for Accurate 2D and 3D Medical Image Segmentation.

ABSTRACT: U-structure has become a foundational approach in medical image segmentation, consistently demonstrating strong performance across various segmentation tasks. Most current models are based on this framework, customizing encoder-decoder components to achieve higher accuracy across various segmentation challenges. However, this often comes at the cost of increased parameter counts, which inevitably limit their practicality in real-world applications. In this study, we provide an E-shaped segmentation framework that discards the traditional step-by-step resolution recovery decoding process, instead directly aggregating multi-scale features extracted by the encoder at each stage for deep cross-level integration. Additionally, we propose an innovative multi-scale large-kernel convolution (MLKCo

SUBMITTER: Wu W 

PROVIDER: S-EPMC12408157 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature