Accurate segmentation of polyps in colonoscopy images plays a pivotal role in the early detection and subsequent treatment of colorectal cancer. Nevertheless, existing segmentation techniques face challenges due to the significant variability in polyps' shapes, sizes, and the often indistinct contrast between polyp boundaries and the surrounding mucosal tissue. To address these limitations,we introduce a novel network architecture termed FDSS-Net, aimed at enhancing segmentation accuracy. Our key innovations include, Firstly, the Feature Enhancement and Propagation Module (FEPM) is designed to capture intricate context across multiple scales. It achieves this by integrating depthwise separable convolutional layers with varying kernel sizes, enabling the model to discern fine details and broader patterns simultaneously. Secondly, the Dual-Stream Semantic Mixture (DSSM) Module facilitates hierarchical feature alignment and deep semantic blending across adjacent levels. By incorporating a cross-attention mechanism and a global context modeling block, DSSM ensures that relevant features are effectively combined and utilized. Lastly, the Hierarchical Multi-scale Aggregation and Prediction Module (HMAP) aggregates features progressively from coarse to fine scales, guided by a learnable gate. This method outperformed 12 state-of-the-art methods on five datasets, especially achieving a Dice coefficient of 0.8302 and mIoU of 0.7587 on the ETIS-LaribPolypDB dataset. It demonstrates the potential to enhance clinical computer-aided diagnosis and provides inspiration for further research in this field.
Wang et al. (Mon,) studied this question.