GCA-Net: global context attention network for intestinal wall vascular segmentation

• Published in: International journal for computer assisted radiology and surgery Vol. 17; no. 3; pp. 569 - 578
• Main Authors: Li, Sheng, Kong, Xueting, Lu, Cheng, Zhu, Jinhui, He, Xiongxiong, Fu, Ruibiao
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.03.2022; Springer Nature B.V
• Subjects: Blood vessels; Boundaries; Computer Imaging; Computer Science; Context; Feature extraction; Health Informatics; Humans; Image enhancement; Image Processing, Computer-Assisted - methods; Image segmentation; Imaging; Intestines; Medicine; Medicine & Public Health; Original Article; Pattern Recognition and Graphics; Radiology; Retinal Vessels; Surgery; Vision; Worms; Deep learning; Contour loss; Intestinal wall vessels; Multi-scale fusion attention module; Medical image segmentation
• ISSN: 1861-6410; 1861-6429
• DOI: 10.1007/s11548-021-02506-x

Purpose Precise segmentation of intestinal wall vessels is vital to colonic perforation prevention. However, there are interferences such as gastric juice in the vessel image of the intestinal wall, especially vessels and the mucosal folds are difficult to distinguish, which easily lead to mis-segmentation. In addition, the insufficient feature extraction of intricate vessel structures may leave out information of tiny vessels that result in rupture. To overcome these challenges, an effective network is proposed for segmentation of intestinal wall vessels. Methods A global context attention network (GCA-Net) that employs a multi-scale fusion attention (MFA) module is proposed to adaptively integrate local and global context information to improve the distinguishability of mucosal folds and vessels, more importantly, the ability to capture tiny vessels. Also, a parallel decoder is used to introduce a contour loss function to solve the blurry and noisy blood vessel boundaries. Results Extensive experimental results demonstrate the superiority of the GCA-Net, with accuracy of 94.84%, specificity of 97.89%, F1-score of 73.80%, AUC of 96.30%, and MeanIOU of 76.46% in fivefold cross-validation, exceeding the comparison methods. In addition, the public dataset DRIVE is used to verify the potential of GCA-Net in retinal vessel image segmentation. Conclusion A novel network for segmentation of intestinal wall vessels is developed, which can suppress interferences in intestinal wall vessel images, improve the discernibility of blood vessels and mucosal folds, enhance vessel boundaries, and capture tiny vessels. Comprehensive experiments prove that the proposed GCA-Net can accurately segment the intestinal wall vessels.