Structure-aware contrastive learning for glomerulus segmentation in renal pathology

Accurate segmentation of glomeruli in renal pathology is challenging due to the difficulty in distinguishing glomeruli from surrounding tissues and their indistinct boundaries. Traditional methods often struggle with local receptive fields, primarily capturing texture rather than the overall shape of these structures. To address this issue, this paper presents a structure-aware contrastive learning strategy for precise glomerular segmentation. We implement a superpixel consistency constraint, dividing pathological images into regions of local consistency to ensure that pixels within the same area maintain feature similarity, thereby capturing structural cues of various renal tissues. The introduced loss function applies shape constraints, enabling the model to better represent the complex morphology of glomeruli against challenging backgrounds. To enhance shape consistency within glomeruli while ensuring discriminability from external tissues, we develop a contrastive learning approach that utilizes extracted structural cues. This encourages the network to effectively learn internal shape constraints and differentiate between distinct regions in feature space. Finally, we implement a multi-scale convolutional attention mechanism that integrates spatial and channel attention, improving the capture of structural features across scales. Experimental results demonstrate that our method significantly enhances segmentation accuracy across multiple public datasets, showcasing the potential of contrastive learning in renal pathology.