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Abstract #2593

Deep Learning for Liver Segmentation and Quantification of Obese Patients

Philipp Mad├Ârin1, Xeni Deligianni1,2, Francesco Santini1,2, Simon Andermatt2, Philippe Claude Cattin2, Anne Christin Meyer-Gerspach3,4, Bettina Karin W├Âlnerhanssen3,4, Oliver Bieri1,2, and Orso Pusterla1,2,5
1Department of Radiology, Division of Radiological Physics, University Hospital Basel, Basel, Switzerland, 2Department of Biomedical Engineering, University of Basel, Basel, Switzerland, 3St. Clara Research Ltd, St. Clara Hospital, Basel, Switzerland, 4University of Basel, Basel, Switzerland, 5Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland

Obesity is one of the greatest health risks and strongly related to fatty liver disease. Magnetic resonance imaging enables non-invasive measurement of fat-water distribution in tissue. To provide an automated evaluation of the liver volume and fat percentage, we trained a Multi-Dimensional Gated Recurrent Units network to segment multi-contrast data. The neural network was trained with a limited number of data comprising 52, 20, 10 datasets and was evaluated for liver volume and fat percentage quantification.

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