ArticleJournal of pathology informatics2020
Using Image Registration and Machine Learning to Develop a Workstation Tool for Rapid Analysis of Glomeruli in Medical Renal Biopsies.
Article in Journal of pathology informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 10 citations in OpenAlex.
- A Spatially Guided Machine-Learning Method to Classify and Quantify Glomerular Patterns of Injury in Histology Images.Journal of imaging · 2023Article
- Artificial Intelligence-Assisted Renal Pathology: Advances and Prospects.Journal of clinical medicine · 2022Review
- 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
- Deep learning segmentation of glomeruli on kidney donor frozen sections.Journal of medical imaging (Bellingham, Wash.) · 2021Article
- Commentary: The Digital Fate of Glomeruli in Renal Biopsy.Journal of pathology informatics · 2021Article
- Advantages of Using a Web-based Digital Platform for Kidney Preimplantation Biopsies.Journal of pathology informatics · 2021Article
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPrescreening of biopsies has the potential to improve pathologists' workflow. Tools that identify features and display results in a visually thoughtful manner can enhance efficiency, accuracy, and reproducibility. Machine learning for detection of glomeruli ensures comprehensive assessment and registration of four different stains allows for simultaneous navigation and viewing.
methodsMedical renal core biopsies (4 stains each) were digitized using a Leica SCN400 at ×40 and loaded into the Corista Quantum research platform. Glomeruli were manually annotated by pathologists. The tissue on the 4 stains was registered using a combination of keypoint- and intensity-based algorithms, and a 4-panel simultaneous viewing display was created. Using a training cohort, machine learning convolutional neural net (CNN) models were created to identify glomeruli in all stains, and merged into composite fields of views (FOVs). The sensitivity and specificity of glomerulus detection, and FOV area for each detection were calculated.
resultsForty-one biopsies were used for training (28) and same-batch evaluation (6). Seven additional biopsies from a temporally different batch were also evaluated. A variant of AlexNet CNN, used for object recognition, showed the best result for the detection of glomeruli with same-batch and different-batch evaluation: Same-batch sensitivity 92%, "modified" specificity 89%, average FOV size represented 0.8% of the total slide area; different-batch sensitivity 90%, "modified" specificity 98% and average FOV size 1.6% of the total slide area.
conclusionsGlomerulus detection in the best CNN model shows that machine learning algorithms may be accurate for this task. The added benefit of biopsy registration with simultaneous display and navigation allows reviewers to move from one machine-generated FOV to the next in all 4 stains. Together these features could increase both efficiency and accuracy in the review process.
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