Abstract: Fully Automated Deep Learning Pipeline for Adipose Tissue Segmentation on Abdominal Dixon MRI

The accurate quantification of visceral and subcutaneous adipose tissue (VAT and SAT) has become a mayor interest worldwide, given that these tissue types represent an important risk factor of metabolic disorders. Currently, the gold standard for measuring volumes of VAT and SAT is the manual segmentation of abdominal fat images from 3D Dixon magnetic resonance (MR) scans – a very expensive and time-consuming process. To this end, we recently proposed Fat-SegNet [1] a fully automated pipeline to accurately segment adipose tissue inside a consistent anatomically defined abdominal region.

Authors and Affiliations

1. German Center for Neurodegenerative Diseases (DZNE), Bonn, Deutschland

   Santiago Estrada, Ran Lu, Sailesh Conjeti, Ximena Orozco, Joana Panos, Monique M.B Breteler & Martin Reuter

2. IMBIE, Faculty of Medicine, University of Bonn, Bonn, Deutschland

   Monique M.B Breteler

3. Department of Radiology, Harvard Medical School, Boston, USA

   Martin Reuter

Editors and Affiliations

1. Institut für Medizinische Informatik, Charité - Universitätsmedizin Berlin, Berlin, Germany

   Thomas Tolxdorff

2. Peter L. Reichertz Institut für Medizinische Informatik, Technische Universität Braunschweig, Braunschweig, Germany

   Thomas M. Deserno

3. Institut für Medizinische Informatik, Universität zu Lübeck, Lübeck, Germany

   Heinz Handels

4. Lehrstuhl für Mustererkennung, Friedrich-Alexander-Universität, Erlangen, Germany

   Andreas Maier

5. Medical Image Computing, E230, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Germany

   Klaus H. Maier-Hein

6. Fakultät für Informatik und Mathematik, Ostbayerische Technische Hochschule Regensburg, Regensburg, Germany

   Christoph Palm

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