ArticleJournal of medical imaging (Bellingham, Wash.)2017
Unsupervised labeling of glomerular boundaries using Gabor filters and statistical testing in renal histology.
Article in Journal of medical imaging (Bellingham, Wash.), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it, 32 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
- Rapid slide-level assessment of glomerular yield using virtual staining of unstained renal biopsy sections.Diagnostic pathology · 2026Article
- Glo-In-One: holistic glomerular detection, segmentation, and lesion characterization with large-scale web image mining.Journal of medical imaging (Bellingham, Wash.) · 2022Article
- Glomerular disease classification and lesion identification by machine learning.Biomedical journal · 2022Article
- Circle Representation for Medical Object Detection.IEEE transactions on medical imaging · 2022Article
- AI applications in renal pathology.Kidney international · 2021Review
- Artificial intelligence and algorithmic computational pathology: an introduction with renal allograft examples.Histopathology · 2021Review
- User friendly, cloud based, whole slide image segmentation.Proceedings of SPIE--the International Society for Optical Engineering · 2021Article
- Artificial intelligence driven next-generation renal histomorphometry.Current opinion in nephrology and hypertension · 2020Review
- Generative modeling for label-free glomerular modeling and classification.Proceedings of SPIE--the International Society for Optical Engineering · 2020Article
- CircleNet: Anchor-free Glomerulus Detection with Circle Representation.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2020Article
- Deep Learning-Based Histopathologic Assessment of Kidney Tissue.Journal of the American Society of Nephrology : JASN · 2019Article
- Examining Structural Patterns and Causality in Diabetic Nephropathy using inter-Glomerular Distance and Bayesian Graphical Models.Proceedings of SPIE--the International Society for Optical Engineering · 2019Article
- Deep Learning Global Glomerulosclerosis in Transplant Kidney Frozen Sections.IEEE transactions on medical imaging · 2018Article
- Multi-radial LBP Features as a Tool for Rapid Glomerular Detection and Assessment in Whole Slide Histopathology Images.Scientific reports · 2018Article
- Digital pathology in nephrology clinical trials, research, and pathology practice.Current opinion in nephrology and hypertension · 2017Review
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
The glomerulus is the blood filtering unit of the kidney. Each human kidney contains [Formula: see text] glomeruli. Several renal conditions originate from structural damage to glomerular microcompartments, such as proteinuria, the excessive loss of blood proteins into urine. The gold standard for evaluating structural damage in renal pathology is histopathological and immunofluorescence examination of needle biopsies under a light microscope. This method is limited by qualitative or semiquantitative manual scoring approaches to the evaluation of glomerular structural features. Computational quantification of equivalent features promises to improve the precision of glomerular structural analysis. One large obstacle to the computational quantification of renal tissue is the identification of complex glomerular boundaries automatically. To mitigate this issue, we developed a computational pipeline capable of extracting and exactly defining glomerular boundaries. Our method, composed of Gabor filtering, Gaussian blurring, statistical [Formula: see text]-testing, and distance transform, is able to accurately identify glomerular boundaries with mean sensitivity/specificity of [Formula: see text] and accuracy of 0.92, on [Formula: see text] glomeruli images stained with standard renal histological stains. Our method will simplify computational partitioning of glomerular microcompartments hidden within dense textural boundaries. Automatic quantification of glomeruli will streamline structural analysis in clinic and can pioneer real-time diagnoses and interventions for renal care.
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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.