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The precise segmentation of the glomerular basement membrane (GBM) can aid pathologists in making accurate pathological diagnoses. However, conventional methods solely focus on segmenting GBM from the background, disregarding the interconnections between GBM and its similar surrounding tissues, which leads to imprecise boundary segmentation of GBM. To address this issue, we employed a multi-category segmentation method to model the distinctions and interconnections between GBM and its similar surrounding tissues. Our experimental results demonstrate that this method can more accurately segment GBM with blurred boundaries. Historically, scholars have primarily used convolution to build models. This approach has limitation that only local information is modeled without effectively extracting global information. To address this issue, we propose a more reasonable structure combining convolution and attention, which we call the Self-Reinforcing Attention Mechanism. Experimental results indicate that the addition of the attention mechanism can help the segmentation of GBM by yielding more continuous boundaries. Finally, we incorporate the feature maps of each layer of the model in the loss function, allowing the model to focus semantic information at varying scales while also providing control over the model&#x2019;s focus by adjusting the weight. Our experimental results demonstrated that the proposed method has higher performance and better generalization ability than the state-of-the-art approaches.

Self Reinforcing Multi-class Transformer For Kidney Glomerular Basement Membrane Segmentation