Fully automated quantitative assessment of hepatic steatosis in liver transplants
| Autor: | Massimo Salvi, Jasna Metovic, Renato Romagnoli, Luca Molinaro, Mauro Papotti, Damiano Patrono, Filippo Molinari |
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| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
Steatosis assessment Computer science medicine.medical_treatment Microvesicular Steatosis H&E stain Health Informatics Liver transplantation Convolutional neural network 03 medical and health sciences 0302 clinical medicine medicine Image Processing Computer-Assisted Digital pathology Humans Segmentation Automatic segmentation Computer-aided image analysis Liver biopsy medicine.diagnostic_test business.industry Pattern recognition medicine.disease Computer Science Applications Liver Transplantation Fatty Liver 030104 developmental biology Artificial intelligence Neural Networks Computer Steatosis business 030217 neurology & neurosurgery Algorithms |
| Zdroj: | Computers in biology and medicine. 123 |
| ISSN: | 1879-0534 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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