🤖 AI Summary
This study addresses the challenges of ambiguous boundaries and weak local geometric continuity in visual state space models for endoscopic polyp segmentation by proposing CSG-Mamba. Specifically, a Convolutional Scoring Gating module is embedded within the VM-UNet bottleneck, leveraging large-kernel depthwise and pointwise convolutions to generate spatial score maps. Through multiplicative gating, this mechanism recalibrates features to enhance local modeling capabilities and mitigate contour over-smoothing. Experimental evaluations on the Kvasir-SEG and CVC-ColonDB datasets demonstrate that CSG-Mamba achieves a Dice score of 0.9220 and an mIoU of 0.7418, respectively. These results indicate significant improvements over existing baselines, particularly in boundary precision, validating the effectiveness of integrating convolutional gating into state space architectures for precise medical image segmentation.
📝 Abstract
Accurate polyp segmentation is critical for computer-aided colonoscopy, yet endoscopic images often contain low-contrast boundaries, mucosal texture interference, specular highlights, and device-dependent appearance shifts. Vision State Space Models (SSMs) provide efficient long-range modeling with linear complexity, but existing Vision Mamba segmentation models typically convert 2D features into 1D scanning sequences, which may weaken local geometric continuity and over-smooth irregular contours. We propose CSG-Mamba, a convolutional scoring gating Vision State Space network for endoscopic polyp segmentation. Built on a VM-UNet-style asymmetric U-shaped encoder-decoder, CSG-Mamba inserts a Convolutional Scoring Gating (CSG) module at the semantically rich bottleneck. CSG generates a local spatial score map through pointwise and large-kernel depthwise convolutions and recalibrates state-space features by multiplicative gating. Experiments with three random seeds show that CSG-Mamba achieves 0.9220 Dice and 15.87 HD95 on Kvasir-SEG, and 0.7418 Dice and 0.6570 mIoU on CVC-ColonDB, outperforming the baselines on most overlap and recall metrics while maintaining competitive boundary accuracy.