ArticleFrontiers in medicine2026
GlomNet: glomeruli segmentation network for WSI with diverse staining protocols for kidney disease diagnosis.
Article in Frontiers in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Accurate segmentation of glomeruli in kidney histopathology images is vital for diagnosing renal diseases but remains challenging due to several limitations in current CNN-based methods. First, multiscale learning struggles to preserve the critical features of small glomeruli, as uniform processing dilutes their discriminative information and hinders effective separation from surrounding tissue. Second, limited interaction between consecutive layers leads to incomplete feature fusion, weakening the representation of complex glomeruli structures. Finally, traditional upsampling techniques introduce artifacts like checkerboards and blurred edges, further compromising segmentation accuracy. To address these challenges, we propose GlomNet, a simple yet effective glomeruli segmentation network designed for whole-slide images (WSIs) with diverse staining protocols, enabling accurate segmentation for Kidney disease diagnosis. GlomNet integrates three key components: Local-Global Cris-Cross Former (LGC
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