Work

Enhancing Deep Learning–Based Subabdominal MR Image Segmentation During Rectal Cancer Treatment: Exploiting Multiscale Feature Pyramid Network and Bidirectional Cross‐Attention Mechanism

Year: 2025

Type: article

Abstract: Background: This study is aimed at solving the misalignment and semantic gap caused by multiple convolutional and pooling operations in U‐Net while segmenting subabdominal MR images during rectal cancer treatment. Methods: We propose a new approach for MR Image Segmentation based on a multiscale feature pyramid network and a bidirectional cross‐attention mechanism. Our approach comprises two innovative modules: (1) We use dilated convolution and a multiscale feature pyramid network in the encoding phase to mitigate the semantic gap, and (2) we implement a bidirectional cross‐attention mechanism to preserve spatial information in U‐Net and reduce misalignment. Results: Experimental results on a subabdominal MR image dataset demonstrate that our proposed method outperforms existing methods. Conclusion: A multiscale feature pyramid network effectively reduces the semantic gap, and the bidirectional cross‐attention mechanism facilitates feature alignment between the encoding and decoding stages.

Source: International Journal of Biomedical Imaging

DOI: 10.1155/ijbi/7560099

Authors: Yu Xiao, Xin Yang, Sijuan Huang, Lihua Guo

Institutions: South China University of Technology, Sun Yat-sen University, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China

Language: English

Topic: Medical Image Segmentation Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $1,000