A deep learning framework for glomeruli segmentation with boundary attention

arXiv cs.LG / 4/17/2026

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Key Points

The paper introduces a U-Net-based deep learning model for glomeruli detection and segmentation that specifically targets the accurate separation of adjacent glomeruli.
It goes beyond standard semantic segmentation by using a specialized attention decoder to emphasize boundary-related regions and improve instance-level segmentation quality.
The approach leverages pathology foundation models to strengthen feature representations relevant to kidney tissue analysis.
Experiments report improved performance over state-of-the-art methods, with higher Dice score and Intersection over Union for glomerular delineation.
The work is positioned as more reliable for diagnostic use cases where precise boundary delineation between neighboring structures is critical.

Abstract

Accurate detection and segmentation of glomeruli in kidney tissue are essential for diagnostic applications. Traditional deep learning methods primarily rely on semantic segmentation, which often fails to precisely delineate adjacent glomeruli. To address this challenge, we propose a novel glomerulus detection and segmentation model that emphasises boundary separation. Leveraging pathology foundation models, the proposed U-Net-based architecture incorporates a specialised attention decoder designed to highlight critical regions and improve instancelevel segmentation. Experimental evaluations demonstrate that our approach surpasses state-of-the-art methods in both Dice score and Intersection over Union, indicating superior performance in glomerular delineation.