Fully automated quantitative assessment of hepatic steatosis in liver transplants. (August 2020)

• Record Type: Journal Article
• Authors: Salvi, Massimo; Molinaro, Luca; Metovic, Jasna; Patrono, Damiano; Romagnoli, Renato; Papotti, Mauro; Molinari, Filippo
• Is Part Of: Computers in biology and medicine. Volume 123(2020)
• Publication Date: 2020-08
• Subjects: Computer-aided image analysis -- Digital pathology -- Automatic segmentation -- Liver biopsy -- Steatosis assessment; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2020.103836
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Background: The presence of macro- and microvesicular steatosis is one of the major risk factors for liver transplantation. An accurate assessment of the steatosis percentage is crucial for determining liver graft transplantability, which is currently based on the pathologists' visual evaluations on liver histology specimens. Method: The aim of this study was to develop and validate a fully automated algorithm, called HEPASS (HEPatic Adaptive Steatosis Segmentation), for both micro- and macro-steatosis detection in digital liver histological images. The proposed method employs a hybrid deep learning framework, combining the accuracy of an adaptive threshold with the semantic segmentation of a deep convolutional neural network. Starting from all white regions, the HEPASS algorithm was able to detect lipid droplets and classify them into micro- or macrosteatosis. Results: The proposed method was developed and tested on 385 hematoxylin and eosin (H&E) stained images coming from 77 liver donors. Automated results were compared with manual annotations and nine state-of-the-art techniques designed for steatosis segmentation. In the TEST set, the algorithm was characterized by 97.27% accuracy in steatosis quantification (average error 1.07%, maximum average error 5.62%) and outperformed all the compared methods. Conclusions: To the best of our knowledge, the proposed algorithm is the first fully automated algorithm for the assessment of both micro- and macrosteatosis in H&E stained liver tissue images. Being very fast (average computational time 0.72 s), this algorithm paves the way for automated, quantitative and real-time liver graft assessments. Highlights: Liver steatosis has an important role in assessing the suitability for transplant. Histological assessment is subjected to inter- and intra-observer variability. An automated method is presented to detect both the micro- and macrosteatosis. An accuracy of 97.53% and 97.27% was achieved in train and test set, respectively. The algorithm paves the way for quantitative and real-time liver graft assessments.