ArticleKidney international reports2019
Segmentation of Glomeruli Within Trichrome Images Using Deep Learning.
Article in Kidney international reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 67 papers, 2 of them syntheses that pooled it.
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Who cites it
67 citing papers in PubMed, 2 syntheses or guidelines pooled it, 175 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
- Predict, diagnose, and treat chronic kidney disease with machine learning: a systematic literature review.Journal of nephrology · 2023Pooled it
- Computational design of artificial supply networks for engineered human tissue.Scientific reports · 2026Article
- GloPath: An Entity-Centric Foundation Model for Glomerular Lesion Assessment and Clinicopathological Insights.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Laboratory-based cellular-level correlative visible-light and X-ray microscopy for 3D evaluation of mouse kidney biopsy.Scientific reports · 2026Article
- PATHOS: Pathology attention framework for treatment response stratification in ovarian high-grade serous carcinomas following neoadjuvant chemotherapy on H&E images.Journal of pathology informatics · 2026Article
- Computational Characterization of Lymphocyte Topology on Whole Slide Images of Glomerular Diseases.Journal of the American Society of Nephrology : JASN · 2026Article
- Multiscale segmentation using hierarchical phase-contrast tomography and deep learning.PLoS computational biology · 2026Article
- AI-based pathomics in kidney diseases: progress and application.Renal failure · 2025Review
- Deep learning-based histopathological assessment of tubulo-interstitial injury in chronic kidney diseases.Communications medicine · 2025Article
- Computational pathology: A survey review and the way forward.Journal of pathology informatics · 2024Review
- Vision transformer introduces a new vitality to the classification of renal pathology.BMC nephrology · 2024Article
- PrPSeg: Universal Proposition Learning for Panoramic Renal Pathology Segmentation.Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition · 2024Article
- Deep-learning model for evaluating histopathology of acute renal tubular injury.Scientific reports · 2024Article
- Computational pathology model to assess acute and chronic transformations of the tubulointerstitial compartment in renal allograft biopsies.Scientific reports · 2024Article
- Multi-scale Multi-site Renal Microvascular Structures Segmentation for Whole Slide Imaging in Renal Pathology.Proceedings of SPIE--the International Society for Optical Engineering · 2024Article
- Artificial intelligence-assisted quantification and assessment of whole slide images for pediatric kidney disease diagnosis.Bioinformatics (Oxford, England) · 2024Article
- Development of a multiple convolutional neural network-facilitated diagnostic screening program for immunofluorescence images of IgA nephropathy and idiopathic membranous nephropathy.Clinical kidney journal · 2023Article
- Reconfiguration and loss of peritubular capillaries in chronic kidney disease.Scientific reports · 2023Article
- A Spatially Guided Machine-Learning Method to Classify and Quantify Glomerular Patterns of Injury in Histology Images.Journal of imaging · 2023Article
7 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
12 authors at 2 institutions in 1 country.
Funding
Abstract
introductionThe number of glomeruli and glomerulosclerosis evaluated on kidney biopsy slides constitute standard components of a renal pathology report. Prevailing methods for glomerular assessment remain manual, labor intensive, and nonstandardized. We developed a deep learning framework to accurately identify and segment glomeruli from digitized images of human kidney biopsies.
methodsTrichrome-stained images (
resultsThe CNN model was able to accurately discriminate nonglomerular images from NPS and GS images (performance on test data: Accuracy: 92.67% ± 2.02% and Kappa: 0.8681 ± 0.0392). The segmentation model that was based on the CNN multilabel classifier accurately marked the GS glomeruli on the test data (Matthews correlation coefficient = 0.628).
conclusionThis work demonstrates the power of deep learning for assessing complex histologic structures from digitized human kidney biopsies.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.