Colorectal polyp segmentation method based on fusion of transformer and cross-level phase awareness.
10.7507/1001-5515.202211067
- Author:
Liming LIANG
1
;
Anjun HE
1
;
Chenkun ZHU
1
;
Xiaoqi SHENG
2
Author Information
1. School of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou, Jiangxi 341000, P. R. China.
2. School of Computer Science and Engineering, South China University of Technology, Guangzhou 510000, P. R. China.
- Publication Type:Journal Article
- Keywords:
Colorectal polyps;
Image segmentation;
Phase-aware fusion module;
Position oriented function module;
Transformer
- MeSH:
Humans;
Colonic Polyps/diagnostic imaging*;
Computer Simulation;
Electric Power Supplies;
Semantics;
Image Processing, Computer-Assisted
- From:
Journal of Biomedical Engineering
2023;40(2):234-243
- CountryChina
- Language:Chinese
-
Abstract:
In order to address the issues of spatial induction bias and lack of effective representation of global contextual information in colon polyp image segmentation, which lead to the loss of edge details and mis-segmentation of lesion areas, a colon polyp segmentation method that combines Transformer and cross-level phase-awareness is proposed. The method started from the perspective of global feature transformation, and used a hierarchical Transformer encoder to extract semantic information and spatial details of lesion areas layer by layer. Secondly, a phase-aware fusion module (PAFM) was designed to capture cross-level interaction information and effectively aggregate multi-scale contextual information. Thirdly, a position oriented functional module (POF) was designed to effectively integrate global and local feature information, fill in semantic gaps, and suppress background noise. Fourthly, a residual axis reverse attention module (RA-IA) was used to improve the network's ability to recognize edge pixels. The proposed method was experimentally tested on public datasets CVC-ClinicDB, Kvasir, CVC-ColonDB, and EITS, with Dice similarity coefficients of 94.04%, 92.04%, 80.78%, and 76.80%, respectively, and mean intersection over union of 89.31%, 86.81%, 73.55%, and 69.10%, respectively. The simulation experimental results show that the proposed method can effectively segment colon polyp images, providing a new window for the diagnosis of colon polyps.
