Deep-learning-based Accurate Hepatic Steatosis Quantification for Histological Assessment of Liver Biopsies
Open accessAbstract
Hepatic steatosis droplet quantification with histology biopsies has high clinical significance for risk stratification and management of patients with fatty liver diseases and in the decision to use donor livers for transplantation. However, pathology reviewing processes, when conducted manually, are subject to a high inter- and intra-reader variability, due to the overwhelmingly large number and significantly varying appearance of steatosis instances. This process is challenging as there is a large number of overlapped steatosis droplets with either missing or weak boundaries. In this study, we propose a deep-learning-based region-boundary integrated network for precise steatosis quantification with whole slide liver histopathology images. The proposed model consists of two sequential steps: a region extraction and a boundary prediction module for foreground regions and steatosis boundary prediction, followed by an integrated prediction map generation. Missing steatosis boundaries are next recovered from the predicted map and assembled from adjacent image patches to generate results for the whole slide histopathology image. The resulting steatosis measures both at the pixel level and steatosis object-level present strong correlation with pathologist annotations, radiology readouts and clinical data. In addition, the segregated steatosis object count is shown as a promising alternative measure to the traditional metrics at the pixel level. These results suggest a high potential of artificial intelligence-assisted technology to enhance liver disease decision support using whole slide images.
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Several groups have developed deep learning algorithms to assess the degree of steatosis in liver biopsies.
“Recently, several groups have developed deep learning algorithms to assess the degree of steatosis in liver biopsies (47–49).”
From Artificial Intelligence: Present and Future Potential for Solid Organ Transplantation (2022) · cites this paper as [47]
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Artificial Intelligence: Present and Future Potential for Solid Organ Transplantation
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- Authors
- Roy, M., Wang, F., Vo, H., Teng, D., Teodoro, G., Farris, A. B., Castillo-Leon, E., Vos, M. B., & Kong, J.
- Affiliations
- Stony Brook University (US)
- Universidade Federal de Minas Gerais (BR)
- Emory University (US)
- Corresponding author
- Wang, Fusheng
- Kong, Jun
- ORCID
- Venue
- Laboratory Investigation
- Publisher
- Elsevier BV
- Published
- 2020-10
- Volume
- 100
- Issue
- 10
- Pages
- 1367-1383
- ISSN
- 0023-6837 (ISSN-L)
- PMC
- PMC7502534
- OpenAlex
- W3042325762
- Cited by
- 68
- References
- 47
- Citations by year
- 2026: 12 · 2025: 12 · 2024: 7 · 2023: 15 · 2022: 12 · 2021: 9 · 2020: 1
- FWCI
- 3.63
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Laboratory Investigation (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Science Foundation (1350885, 1443054)
- National Eye Institute (R01EY028450)
- National Cancer Institute (U01CA242936, K25CA181503)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Epidemiology › Liver Disease Diagnosis and Treatment
- Keywords
- hepatic steatosis, liver biopsy, whole slide images, region extraction, clinical decision support
- MeSH terms
- Deep Learning, Algorithms, Biopsy, Fatty Liver, Humans, Image Interpretation, Computer-Assisted, Liver, Radiographic Image Interpretation, Computer-Assisted, Software
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