HPANet: Hierarchical Path Aggregation Network with Pyramid Vision Transformers for Colorectal Polyp Segmentation

• Published in: Algorithms Vol. 18; no. 5; p. 281
• Main Authors: Ying, Yuhong, Li, Haoyuan, Zhong, Yiwen, Lin, Min
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.05.2025
• Subjects: Accuracy; Algorithms; Bubbles; Colonoscopy; Colorectal cancer; computer vision; Datasets; Deep learning; Design; Diagnosis; Heterogeneity; Image segmentation; Intestinal polyps; Lesions; Machine learning; medical image segmentation; Medical screening; Methods; Modules; Neural networks; Noise reduction; polyp segmentation; Polyps; pyramid vision transformers; Real time; Semantics; China
• ISSN: 1999-4893; 1999-4893
• DOI: 10.3390/a18050281

The automatic segmentation technique for colorectal polyps in colonoscopy is considered critical for aiding physicians in real-time lesion identification and minimizing diagnostic errors such as false positives and missed lesions. Despite significant progress in existing research, accurate segmentation of colorectal polyps remains technically challenging due to persistent issues such as low contrast between polyps and mucosa, significant morphological heterogeneity, and susceptibility to imaging artifacts caused by bubbles in the colorectal lumen and poor lighting conditions. To address these limitations, this study proposed a novel pyramid vision transformer-based hierarchical path aggregation network (HPANet) for polyp segmentation. Specifically, firstly, the backward multi-scale feature fusion module (BMFM) was developed to enhance the ability of processing polyps with different scales. Secondly, the forward noise reduction module (FNRM) was designed to learn the texture features of the upper and lower layers to reduce the influence of noise such as bubbles. Finally, in order to solve the problem of boundary ambiguity caused by repeated up and down sampling, the boundary feature refinement module (BFRM) was developed to further refine the boundary. The proposed network was compared with several representative networks on five public polyp datasets. Experimental results show that the proposed network achieves better segmentation performance, especially on the Kvasir SEG dataset, where the mDice and mIoU coefficients reach 0.9204 and 0.8655.