A cell image segmentation method based on edge feature residual fusion.

• An independent Edge Feature Extraction Module (EFEM) that combines local edge information and global positional semantic information is designed to enhance the extraction and utilization of edge features, thereby improving the problem of losing edge details in down sampling during feature extraction. • A Residual Fusion Module (RFM) is designed to fuse edge features and multi-scale object features during the upsampling process. The two comple-mentary features assist each other in jointly optimizing the entire model to improve segmentation prediction results. • ERF-TransUNet was validated on the Glas dataset and the CRAG dataset. The experimental results show that the model can effectively reduce boundary segmentation errors in cell image segmentation, achieve better indicators in both the Dice coefficient and Hausdorff distance compared with other models. In recent years, cancer has seriously damaged human health, and the morphological structure of cells serves as an important basis for cancer diagnosis and grading. Automatic cell segmentation based on deep learning has become an important means of computer-aided pathological diagnosis. Aiming at the existing problems of rough segmentation boundaries and inaccurate segmentation in cell image segmentation, this paper designs a cell image segmentation network model (ERF-TransUNet) based on edge feature residual fusion from the perspective of mutual complementarity and constraint between edge features and object features. The model uses a hybrid architecture of CNN and Transformer to extract multi-scale features from cell images, and adds independent edge feature extraction modules and residual fusion modules to enhance the extraction of edge features and their constraints when fusing with cell object features, improving the accuracy of cell contour positioning. Through experiments on two gland cell datasets, CRAG and Glas, and comparing the segmentation effects with current popular deep learning models, the network model proposed in this paper has achieved good performance in both Dice coefficient and Hausdorff distance, which can effectively improve the segmentation effect of cell images. [ABSTRACT FROM AUTHOR]