A Boundary-Enhanced Decouple Fusion Segmentation Network for Diagnosis of Adenomatous Polyps

Adenomatous polyps, a common premalignant lesion, are often classified into villous adenoma (VA) and tubular adenoma (TA). VA has a higher risk of malignancy, whereas TA typically grows slowly and has a lower likelihood of cancerous transformation. Accurate classification is essential for tailored treatment. In this study, we develop a deep learning-based approach for the localization and classification of adenomatous polyps using endoscopic images. Specifically, a pre-trained EGE-UNet is first adopted to extract regions of interest from original images. Multi-level feature maps are then extracted by the feature extraction pipeline (FEP). The deep-level features are fed into the Pyramid Pooling Module (PPM) to capture global contextual information, and the squeeze body edge (SBE) module is then used to decouple the body and edge parts of features, enabling separate analysis of their distinct characteristics. The Group Aggregation Bridge (GAB) and Boundary Enhancement Module (BEM) are then applied to enhance the body features and edge features, respectively, emphasizing their structural and morphological characteristics. By combining the features of the body and edge parts, the final output can be obtained. Experiments show the proposed method achieved promising results on two private datasets. For adenoma vs. non-adenoma classification, It achieved a mIoU of 91.41%, mPA of 96.33%, mHD of 11.63, and mASD of 2.33. For adenoma subclassification (non-adenomas vs. villous adenomas vs. tubular adenomas), it achieved a mIoU of 91.21%, mPA of 94.83%, mHD of 13.75, and mASD of 2.56. These results demonstrate the potential of our approach for precise adenomatous polyp classification.