A novel Dual-Branch Asymmetric Encoder-Decoder Segmentation Network for accurate colonic crypt segmentation

Colorectal cancer (CRC) is a leading cause of cancer-related deaths, with colonic crypts (CC) being crucial in its development. Accurate segmentation of CC is essential for decisions CRC and developing diagnostic strategies. However, colonic crypts' blurred boundaries and morphological diversity bring substantial challenges for automatic segmentation. To mitigate this problem, we proposed the Dual-Branch Asymmetric Encoder-Decoder Segmentation Network (DAUNet), a novel and efficient model tailored for confocal laser endomicroscopy (CLE) CC images. In DAUNet, we crafted a dual-branch feature extraction module (DFEM), employing Focus operations and dense depth-wise separable convolution (DDSC) to extract multiscale features, boosting semantic understanding and coping with the morphological diversity of CC. We also introduced the feature fusion guided module (FFGM) to adaptively combine features from both branches using cross-group spatial and channel attention to improve the model representation in focusing on specific lesion features. These modules are seamlessly integrated into the encoder for effective multiscale information extraction and fusion, and DDSC is further introduced in the decoder to provide rich representations for precise segmentation. Moreover, the local multi-layer perceptron (LMLP) module is designed to decouple and recalibrate features through a local linear transformation that filters out the noise and refines features to provide edge-enriched representation. Experimental evaluations on two datasets demonstrate that the proposed method achieves Intersection over Union (IoU) scores of 81.54% and 84.83%, respectively, which are on par with state-of-the-art methods, exhibiting its effectiveness for CC segmentation. The proposed method holds great potential in assisting physicians with precise lesion localization and region analysis, thereby improving the diagnostic accuracy of CRC.