RGD ‐ UNet : Efficient Decoupled Fully Connection Attention for Pixel‐Level Segmentation of Signet‐Ring Cells

Accurate identification of signet ring cells (SRCs) in histopathological images is essential for the auxiliary evaluation of signet ring cell carcinoma (SRCC). Although deep learning has greatly advanced automated medical image interpretation, reliable SRC segmentation remains difficult because pixel‐level annotations are limited and SRCs often appear in crowded, adherent, or overlapping patterns. These characteristics increase the complexity of distinguishing individual cellular regions from surrounding tissues. To address these issues, this work proposes RGD‐UNet, a dedicated semantic segmentation network designed for SRC analysis. The model adopts an encoder–decoder framework and is optimized to capture discriminative pathological features while maintaining efficient computation. Unlike methods that rely on additional refinement procedures, RGD‐UNet can directly delineate clustered and overlapping SRC regions, thereby simplifying the segmentation workflow and improving practical usability. Furthermore, to facilitate the development and evaluation of SRC segmentation algorithms in clinical‐oriented scenarios, the DigestPath 2019 dataset was further extended by adding complete SRC mask annotations. Experimental results demonstrate that RGD‐UNet achieves superior segmentation precision compared with representative state‐of‐the‐art approaches while retaining low computational cost. These findings suggest that the proposed method has strong potential for assisting computer‐aided pathological diagnosis of SRCC.