Work

Ret-UNet: Enhancing medical image segmentation with self-retention

Year: 2025

Type: article

Abstract: Medical image segmentation has advanced significantly due to the integration of deep learning techniques, particularly convolutional neural networks (CNNs) like U-Net. However, CNNs often struggle to capture global spatial relationships, which are crucial for accurately segmenting complex anatomical structures. To address this limitation, we propose Ret-UNet, a novel architecture that enhances the traditional U-Net framework by incorporating the Self-Retention mechanism. Self-Retention introduces an explicit shape prior related to the Euclidean distance, which effectively encode global spatial relationships within the image. The Ret-UNet leverages both local feature extraction and global context awareness by incorporating Ret Blocks into the U-Net like architecture, leading to improved segmentation performance. Evaluations on ACDC, CAMUS and Synapse datasets demonstrate that Ret-UNet achieves superior segmentation accuracy and robustness, outperforming state-of-the-art models. The code is available at https://github.com/weirdgit/RetUNet .

Source: Array

DOI: 10.1016/j.array.2025.100653

Authors: Tianjun Guo, Weixin Zhao, Jian Peng

Institution: Sichuan University

Language: English

Topic: Medical Image Segmentation Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,680

Funders: Sichuan University, Key Research and Development Program of Sichuan Province