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Deep-learning-based accurate hepatic steatosis quantification for histological assessment of liver biopsies

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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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Fig. 1: The DELINEATE model.
Fig. 2: Comparison of segmentation results.
Fig. 3: Visualization of segmented steatosis droplets in masks of distinct colors.
Fig. 4: DELINEATE correlations with manual histology assessment results, and radiology data derived features.
Fig. 5: Steatosis component quantification in whole liver tissue images.

Data availability

All source codes and annotation data related to this paper are available at GitHub [44]. We share image data in a public repository [45].

Change history

  • 17 July 2020

    The original record of this article has been updated to correct author name ‘Eduardo Castillo-Lion’ to ‘Eduardo Castillo-Leon’.


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This research is supported in part by grants from National Institute of Health 7K25CA181503, 1U01CA242936, 5R01EY028450, and National Science Foundation ACI 1443054 and IIS 1350885.

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MR, FW, ABF, MBV and JK designed research; MR, HV, DT, GT, ABF, ECL and JK performed research; MR, ECL and JK analyzed data; MR, FW, ABF, ECL, and JK wrote the paper.

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Correspondence to Fusheng Wang or Jun Kong.

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Roy, M., Wang, F., Vo, H. et al. Deep-learning-based accurate hepatic steatosis quantification for histological assessment of liver biopsies. Lab Invest 100, 1367–1383 (2020).

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