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

• Published in: International journal of biomedical imaging Vol. 2025; no. 1; p. 7560099
• Main Authors: Xiao, Yu, Yang, Xin, Huang, Sijuan, Guo, Lihua
• Format: Journal Article
• Language: English
• Published: United States John Wiley & Sons, Inc 01.01.2025; Wiley
• Subjects: Abdomen; Accuracy; Anus; Bladder; Cancer; Cancer therapies; Care and treatment; Colorectal cancer; Datasets; Deep learning; Drug therapy; Gastroenterology; Image processing; Image segmentation; Labeling; Liu, Timothy; Magnetic resonance imaging; Medical imaging equipment; Neural networks; Pelvis; Privacy; Prostate; Rectum; Semantics; bidirectional cross-attention; subabdominal MR image segmentation; biomedical image segmentation; U-Net; multiscale feature pyramid network
• ISSN: 1687-4188; 1687-4196
• DOI: 10.1155/ijbi/7560099

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.