Flattened and simplified SSCU-Net: exploring the convolution potential for medical image segmentation

Abstract

Medical image semantic segmentation is a crucial technique in medical imaging processing, providing essential diagnostic support by precisely delineating different tissue structures and pathological areas within an image. However, the pursuit of higher accuracy has led to increasingly complex architectures in existing networks, resulting in significant training overhead. In response, this study introduces a flattened, minimalist design philosophy and constructs the shallow super convolution U-shaped Net (SSCU-Net) based on this concept. Compared to the traditional four-layer U-shaped networks, SSCU-Net has a simplified two-layer structure, adhering to a lightweight research objective. On one hand, to address the issue of insufficient semantic feature extraction caused by the shallow network architecture, a parallel multi-branch feature extraction module called the super convolution block is designed to thoroughly extract diverse semantic information. On the other hand, to facilitate the transfer of critical semantic information between encoding and decoding, as well as across layers, the spatial convolution path, along with feature enhanced downsample and feature resolution upsample, are constructed. The performance of SSCU-Net was validated against 18 comparison models across seven metrics on five datasets. Results from metric analysis, image comparisons, and ablation tests collectively demonstrate that SSCU-Net achieves an average improvement of 15.9792% in the Dice coefficient compared to other models, confirming the model’s advantages in both lightweight design and accuracy. The network code is available at https://github.com/YF-W/SSCU-Net.