ArticleKidney international2021
Development and evaluation of deep learning-based segmentation of histologic structures in the kidney cortex with multiple histologic stains.
Article in Kidney international, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 97 papers, 2 of them syntheses that pooled it.
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97 citing papers in PubMed, 2 syntheses or guidelines pooled it, 191 citations in OpenAlex.
- Time for a full digital approach in nephropathology: a systematic review of current artificial intelligence applications and future directions.Journal of nephrology · 2024Pooled it
- Artificial intelligence applications for pre-implantation kidney biopsy pathology practice: a systematic review.Journal of nephrology · 2022Pooled it
- From traditional histopathology to virtual staining: deep learning approaches for assessing tissue lesions in farmed fish.Veterinary research communications · 2026Review
- Next-generation kidney tissue analysis - spatial omics and digital pathology.Nature reviews. Nephrology · 2026Review
- Artificial Intelligence in Kidney Transplant Pathology: Current Evidence, Limitations, and Relevance to the Banff Framework.Kidney international reports · 2026Review
- Kidney tissue characterization using normalized raman imaging and segment-anything.Scientific reports · 2026Article
- Computational Characterization of Lymphocyte Topology on Whole Slide Images of Glomerular Diseases.Journal of the American Society of Nephrology : JASN · 2026Article
- Quantification of interstitial fibrosis in digitized kidney biopsies using deep neural networks.Diagnostic pathology · 2026Article
- Transforming nephrology through artificial intelligence: a state-of-the-art roadmap for clinical integration.Clinical kidney journal · 2026Review
- Glomeruli detection and classification in histopathological images using deep learning semantic segmentation.BMC medical imaging · 2026Article
- Petroselinum crispum (Parsley) Leaf Extract Attenuates Ethylene Glycol-Induced Nephrolithiasis Through Antioxidant and Renoprotective Effects.BioMed research international · 2026Article
- Predicting 1-Year Renal Outcomes in Patients with Diabetic Kidney Disease in CKD Stages 3 to 4: A Multimodal Machine Learning Approach Fusing Clinical Composites and Pathology Images.Research (Washington, D.C.) · 2026Article
- GlomNet: glomeruli segmentation network for WSI with diverse staining protocols for kidney disease diagnosis.Frontiers in medicine · 2026Article
- Using spatial proteomics to enhance cell type assignments in histology images.Cell reports methods · 2025Article
- Transformer-based multiclass segmentation pipeline for basic kidney histology.Scientific reports · 2025Article
- Artificial intelligence-assisted diagnosis of glomerular nephritis using a pathological image analysis approach: a multicentre model development and validation study.EClinicalMedicine · 2025Article
- Article
- Construction and validation of a cross-sectional risk classification model for hypoproteinemia in single-center maintenance hemodialysis patient.Scientific reports · 2025Article
- Clinical Relevance of Computationally Derived Attributes of Arteries and Arterioles in focal segmental glomerulosclerosis and minimal change disease.medRxiv : the preprint server for health sciences · 2025Article
- Advanced transformer with attention-based neural network framework for precise renal cell carcinoma detection using histological kidney images.Scientific reports · 2025Article
37 more citing papers are in PubMed but not listed here.
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Authors and funding
15 authors at 10 institutions in 3 countries.
Funding
Abstract
The application of deep learning for automated segmentation (delineation of boundaries) of histologic primitives (structures) from whole slide images can facilitate the establishment of novel protocols for kidney biopsy assessment. Here, we developed and validated deep learning networks for the segmentation of histologic structures on kidney biopsies and nephrectomies. For development, we examined 125 biopsies for Minimal Change Disease collected across 29 NEPTUNE enrolling centers along with 459 whole slide images stained with Hematoxylin & Eosin (125), Periodic Acid Schiff (125), Silver (102), and Trichrome (107) divided into training, validation and testing sets (ratio 6:1:3). Histologic structures were manually segmented (30048 total annotations) by five nephropathologists. Twenty deep learning models were trained with optimal digital magnification across the structures and stains. Periodic Acid Schiff-stained whole slide images yielded the best concordance between pathologists and deep learning segmentation across all structures (F-scores: 0.93 for glomerular tufts, 0.94 for glomerular tuft plus Bowman's capsule, 0.91 for proximal tubules, 0.93 for distal tubular segments, 0.81 for peritubular capillaries, and 0.85 for arteries and afferent arterioles). Optimal digital magnifications were 5X for glomerular tuft/tuft plus Bowman's capsule, 10X for proximal/distal tubule, arteries and afferent arterioles, and 40X for peritubular capillaries. Silver stained whole slide images yielded the worst deep learning performance. Thus, this largest study to date adapted deep learning for the segmentation of kidney histologic structures across multiple stains and pathology laboratories. All data used for training and testing and a detailed online tutorial will be publicly available.
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