A deep learning framework for glomeruli segmentation with boundary attention
It addresses the problem of accurate glomeruli delineation for diagnostic applications in pathology, but the improvement is incremental over existing methods.
The paper proposes a U-Net-based deep learning framework with a boundary attention decoder for glomeruli segmentation in kidney tissue, achieving superior Dice score and IoU compared to state-of-the-art methods.
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.