Multistain multicompartment automatic segmentation in renal biopsies with thrombotic microangiopathies and other vasculopathies

Automatic tissue segmentation is a necessary step for the bulk analysis of whole slide images (WSIs) from paraffin histology sections in kidney biopsies. However, existing models often fail to generalize across the main nephropathological staining methods and to capture the severe morphological distortions in arteries, arterioles, and glomeruli common in thrombotic microangiopathy (TMA) or other vasculopathies. Therefore, we developed an automatic multi-staining segmentation pipeline covering six key compartments: Artery, Arteriole, Glomerulus, Cortex, Medulla, and Capsule/Other. This framework enables downstream tasks such as counting and labeling at instance-, WSI- or biopsy-level. Biopsies (n = 158) from seven centers: Cologne, Turin, Milan, Weill-Cornell, Mainz, Maastricht, Budapest, were classified by expert nephropathologists into TMA (n = 87) or Mimickers (n = 71). Ground truth expert segmentation masks were provided for all compartments, and expert binary TMA classification labels for Glomerulus, Artery, Arteriole. The biopsies were divided into training (n = 79), validation (n = 26), and test (n = 53) subsets. We benchmarked six deep learning models for semantic segmentation (U-Net, FPN, DeepLabV3+, Mask2Former, SegFormer, SegNeXt) and five models for classification (ResNet-34, DenseNet-121, EfficientNet-v2-S, ConvNeXt-Small, Swin-v2-B). We obtained robust segmentation results across all compartments. On the test set, the best models achieved Dice coefficients of 0.903 (Cortex), 0.834 (Medulla), 0.816 (Capsule/Other), 0.922 (Glomerulus), 0.822 (Artery), and 0.553 (Arteriole). The best classification models achieved Accuracy of 0.724 and 0.841 for Glomerulus and Artery plus Arteriole compartments, respectively. Furthermore, we release NePathTK (NephroPathology Toolkit), a powerful open-source end-to-end pipeline integrated with QuPath, enabling accurate segmentation for decision support in nephropathology and large-scale analysis of kidney biopsies.

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Publication Details

Journal
Computerized Medical Imaging and Graphics
Published
2025-10-23
DOI
https://doi.org/10.1016/j.compmedimag.2025.102658
Citations
1
Primary Topic
Renal and Vascular Pathologies
Type
article
Field-Weighted Citation Impact
1.52
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Multistain multicompartment automatic segmentation in renal biopsies with thrombotic microangiopathies and other vasculopathies

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1 citations
Computerized Medical Imaging and Graphics
Renal and Vascular Pathologies
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article

Multistain multicompartment automatic segmentation in renal biopsies with thrombotic microangiopathies and other vasculopathies

Pieter van Paassen, Deján Dobi, Nicola Altini, Alessandro Del Gobbo, Savino Sciascia, Štefan Porubský, Daan P C van Doorn, Vitoantonio Bevilacqua, Jan U. Becker, Berardino Prencipe, Sjoerd A.M.E.G. Timmermans, Antonella Barreca, Surya V. Seshan, Michela Prunella, Claudia Delprete, Hien Van Nguyen
article en
1 citations

Abstract

Automatic tissue segmentation is a necessary step for the bulk analysis of whole slide images (WSIs) from paraffin histology sections in kidney biopsies. However, existing models often fail to generalize across the main nephropathological staining methods and to capture the severe morphological distortions in arteries, arterioles, and glomeruli common in thrombotic microangiopathy (TMA) or other vasculopathies. Therefore, we developed an automatic multi-staining segmentation pipeline covering six key compartments: Artery, Arteriole, Glomerulus, Cortex, Medulla, and Capsule/Other. This framework enables downstream tasks such as counting and labeling at instance-, WSI- or biopsy-level. Biopsies (n = 158) from seven centers: Cologne, Turin, Milan, Weill-Cornell, Mainz, Maastricht, Budapest, were classified by expert nephropathologists into TMA (n = 87) or Mimickers (n = 71). Ground truth expert segmentation masks were provided for all compartments, and expert binary TMA classification labels for Glomerulus, Artery, Arteriole. The biopsies were divided into training (n = 79), validation (n = 26), and test (n = 53) subsets. We benchmarked six deep learning models for semantic segmentation (U-Net, FPN, DeepLabV3+, Mask2Former, SegFormer, SegNeXt) and five models for classification (ResNet-34, DenseNet-121, EfficientNet-v2-S, ConvNeXt-Small, Swin-v2-B). We obtained robust segmentation results across all compartments. On the test set, the best models achieved Dice coefficients of 0.903 (Cortex), 0.834 (Medulla), 0.816 (Capsule/Other), 0.922 (Glomerulus), 0.822 (Artery), and 0.553 (Arteriole). The best classification models achieved Accuracy of 0.724 and 0.841 for Glomerulus and Artery plus Arteriole compartments, respectively. Furthermore, we release NePathTK (NephroPathology Toolkit), a powerful open-source end-to-end pipeline integrated with QuPath, enabling accurate segmentation for decision support in nephropathology and large-scale analysis of kidney biopsies.

Computerized Medical Imaging and GraphicsVol. 126
Semmelweis University (HU), Johannes Gutenberg University Mainz (DE), Cornell University (US), Maastricht University Medical Centre (NL), Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico (IT), Maastricht University (NL), Azienda Ospedaliera Citta' della Salute e della Scienza di Torino (IT), University Medical Center of the Johannes Gutenberg University Mainz (DE), University Hospital Cologne (DE), Weill Cornell Medicine (US), University of Houston (US), University of Turin (IT), Polytechnic University of Bari (IT)
Openalex Percentile: Top 13%
Renal and Vascular Pathologies
1.52
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