ArticleScientific reports2018
Multi-radial LBP Features as a Tool for Rapid Glomerular Detection and Assessment in Whole Slide Histopathology Images.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 1 of them a synthesis that pooled it.
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38 citing papers in PubMed, 1 synthesis or guideline pooled it, 7 citations in OpenAlex.
- Artificial intelligence applications for pre-implantation kidney biopsy pathology practice: a systematic review.Journal of nephrology · 2022Pooled it
- GlomNet: glomeruli segmentation network for WSI with diverse staining protocols for kidney disease diagnosis.Frontiers in medicine · 2026Article
- Transformer-based multiclass segmentation pipeline for basic kidney histology.Scientific reports · 2025Article
- Deep learning-based histopathological assessment of tubulo-interstitial injury in chronic kidney diseases.Communications medicine · 2025Article
- Revolutionizing renal biopsy: the emerging role of omics and artificial intelligence in nephrology.Frontiers in medicine · 2025Review
- 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
- Automatic Identification of Glomerular in Whole-Slide Images Using a Modified UNet Model.Diagnostics (Basel, Switzerland) · 2023Article
- Prediction of Ovarian Cancer Response to Therapy Based on Deep Learning Analysis of Histopathology Images.Cancers · 2023Article
- Segmentation of human functional tissue units in support of a Human Reference Atlas.Communications biology · 2023Article
- Artificial intelligence in renal pathology: Current status and future.Biomolecules & biomedicine · 2023Review
- Applications of discriminative and deep learning feature extraction methods for whole slide image analysis: A survey.Journal of pathology informatics · 2023Review
- Application of digital pathology and machine learning in the liver, kidney and lung diseases.Journal of pathology informatics · 2023Review
- Computational Pipeline for Glomerular Segmentation and Association of the Quantified Regions with Prognosis of Kidney Function in IgA Nephropathy.Diagnostics (Basel, Switzerland) · 2022Article
- Artificial Intelligence-Assisted Renal Pathology: Advances and Prospects.Journal of clinical medicine · 2022Review
- Glomerular disease classification and lesion identification by machine learning.Biomedical journal · 2022Article
- Quantification of Glomerular Structural Lesions: Associations With Clinical Outcomes and Transcriptomic Profiles in Nephrotic Syndrome.American journal of kidney diseases : the official journal of the National Kidney Foundation · 2022Observational
- Large-scale extraction of interpretable features provides new insights into kidney histopathology - A proof-of-concept study.Journal of pathology informatics · 2022Article
- A user-friendly tool for cloud-based whole slide image segmentation with examples from renal histopathology.Communications medicine · 2022Article
- Deep learning segmentation of glomeruli on kidney donor frozen sections.Journal of medical imaging (Bellingham, Wash.) · 2021Article
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Authors and funding
5 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
We demonstrate a simple and effective automated method for the localization of glomeruli in large (~1 gigapixel) histopathological whole-slide images (WSIs) of thin renal tissue sections and biopsies, using an adaptation of the well-known local binary patterns (LBP) image feature vector to train a support vector machine (SVM) model. Our method offers high precision (>90%) and reasonable recall (>70%) for glomeruli from WSIs, is readily adaptable to glomeruli from multiple species, including mouse, rat, and human, and is robust to diverse slide staining methods. Using 5 Intel(R) Core(TM) i7-4790 CPUs with 40 GB RAM, our method typically requires ~15 sec for training and ~2 min to extract glomeruli reproducibly from a WSI. Deploying a deep convolutional neural network trained for glomerular recognition in tandem with the SVM suffices to reduce false positives to below 3%. We also apply our LBP-based descriptor to successfully detect pathologic changes in a mouse model of diabetic nephropathy. We envision potential clinical and laboratory applications for this approach in the study and diagnosis of glomerular disease, and as a means of greatly accelerating the construction of feature sets to fuel deep learning studies into tissue structure and pathology.
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