Abstract #4730

Automated organ segmentation of liver and spleen in whole-body T1-weighted MR images: Transfer learning between epidemiological cohort studies

Thomas Kuestner^1,2,3, Sarah Müller², Marc Fischer^2,3, Martin Schwartz^2,3, Petros Martirosian², Bin Yang³, Fritz Schick², and Sergios Gatidis²

¹School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom, ²Department of Radiology, University Hospital Tübingen, Tübingen, Germany, ³Institute of Signal Processing and System Theory, University of Stuttgart, Stuttgart, Germany

Automated segmentation of organs and anatomical structures is a prerequisite for efficient analysis of MR data in large cohort studies with thousands of participants. The feasibility of deep learning approaches has been shown to provide good solutions. Since all these methods are based on supervised learning, labeled ground truth data is required which can be time- and cost-intensive to generate. This work examines the feasibility of transfer learning between similar epidemiological cohort studies to derive possibilities in reuse of labeled training data.

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