BKMNet: A boundary-enhanced state space model with Kalman filtering for adenomatous polyp segmentation

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Abstract

Colorectal cancer is one of the leading causes of cancer-related mortality, and both early detection and the removal of adenomatous polyps are effective treatment measures. However, identifying adenomatous polyps remains challenging due to the diverse polyps, boundaries, and intestinal mucosal background. Therefore, we have proposed a boundary-enhanced adenomatous polyp segmentation network with Kalman-mamba (BKMNet), which integrates visual state space mechanisms and Kalman filtering for precise segmentation and identification of adenomatous polyps. Specifically, we adopted a multi-frequency combined attention block to enhance feature representation by focusing on varying channel frequency components. A fast visual state space module extracts multi-level feature maps, the partial channel transformation aggregates the surrounding contextual information, and the structure-aware local-enhanced residual state space module enhances structure awareness. A Kalman-mamba layer aims for long-range dependency modeling. The decoder has a dual attention module with multi-level features, and a refined differential and boundary-enhanced module performs edge detection and global edge feature learning. Results demonstrated that BKMNet outperforms state-of-the-art methods in terms of segmentation accuracy and clinical applicability. This study provides a reliable auxiliary diagnostic tool for supporting clinicians in improving diagnostic efficiency and reducing misdiagnosis rates.

Keywords

mamba / Kalman filtering / boundary learning / segmentation / adenomatous polyps

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