ArticleDiagnostic pathology2017
Automated quantification of steatosis: agreement with stereological point counting.
Article in Diagnostic pathology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic Evaluation of the Safety Threshold for Allograft Macrovesicular Steatosis in Cadaveric Liver Transplantation.Frontiers in physiology · 2019Pooled it
- Detection of macrovesicular steatosis in hematoxylin and eosin-stained histological images of human livers: A feature-based method.Journal of pathology informatics · 2026Article
- Review
- Recommendations on compiling test datasets for evaluating artificial intelligence solutions in pathology.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2022Review
- ZFP36L1 Regulates Fgf21 mRNA Turnover and Modulates Alcoholic Hepatic Steatosis and Inflammation in Mice.The American journal of pathology · 2022Article
- Quantitative magnetic resonance imaging for chronic liver disease.The British journal of radiology · 2021Review
- LIVER STEATOSIS SEGMENTATION WITH DEEP LEARNING METHODS.Proceedings. IEEE International Symposium on Biomedical Imaging · 2019Article
- Focused scores enable reliable discrimination of small differences in steatosis.Diagnostic pathology · 2018Article
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSteatosis is routinely assessed histologically in clinical practice and research. Automated image analysis can reduce the effort of quantifying steatosis. Since reproducibility is essential for practical use, we have evaluated different analysis methods in terms of their agreement with stereological point counting (SPC) performed by a hepatologist.
methodsThe evaluation was based on a large and representative data set of 970 histological images from human patients with different liver diseases. Three of the evaluated methods were built on previously published approaches. One method incorporated a new approach to improve the robustness to image variability.
resultsThe new method showed the strongest agreement with the expert. At 20× resolution, it reproduced steatosis area fractions with a mean absolute error of 0.011 for absent or mild steatosis and 0.036 for moderate or severe steatosis. At 10× resolution, it was more accurate than and twice as fast as all other methods at 20× resolution. When compared with SPC performed by two additional human observers, its error was substantially lower than one and only slightly above the other observer.
conclusionsThe results suggest that the new method can be a suitable automated replacement for SPC. Before further improvements can be verified, it is necessary to thoroughly assess the variability of SPC between human observers.
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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.