Deep Learning-Based Image Analysis of Liver Steatosis in Mouse Models
: Mairinoja Laura, Heikelä Hanna, Blom Sami, Kumar Darshan, Knuuttila Anna, Boyd Sonja, Sjöblom Nelli, Birkman Eva-Maria, Rinne Petteri, Ruusuvuori Pekka, Strauss Leena, Poutanen Matti
Publisher: ELSEVIER SCIENCE INC
: 2023
: American Journal of Pathology
: AMERICAN JOURNAL OF PATHOLOGY
: AM J PATHOL
: 193
: 8
: 1072
: 1080
: 9
: 0002-9440
DOI: https://doi.org/10.1016/j.ajpath.2023.04.014
: https://doi.org/10.1016/j.ajpath.2023.04.014
: https://research.utu.fi/converis/portal/detail/Publication/180845020
The incidence of nonalcoholic fatty liver disease is a continuously growing health problem worldwide, along with obesity. Therefore, novel methods to both efficiently study the manifestation of nonalco-holic fatty liver disease and to analyze drug efficacy in preclinical models are needed. The present study developed a deep neural network-based model to quantify microvesicular and macrovesicular steatosis in the liver on hematoxylin-eosin-stained whole slide images, using the cloud-based platform, Aiforia Create. The training data included a total of 101 whole slide images from dietary interventions of wild-type mice and from two genetically modified mouse models with steatosis. The algorithm was trained for the following: to detect liver parenchyma, to exclude the blood vessels and any artefacts generated during tissue processing and image acquisition, to recognize and differentiate the areas of micro-vesicular and macrovesicular steatosis, and to quantify the recognized tissue area. The results of the image analysis replicated well the evaluation by expert pathologists and correlated well with the liver fat content measured by EchoMRI ex vivo, and the correlation with total liver triglycerides was notable. In conclusion, the developed deep learning-based model is a novel tool for studying liver steatosis in mouse models on paraffin sections and, thus, can facilitate reliable quantification of the amount of steatosis in large preclinical study cohorts. (Am J Pathol 2023, 193: 1072-1080; https://doi.org/ 10.1016/j.ajpath.2023.04.014)
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