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Gutiérrez-Becker, B.* ; Gatidis, S.* ; Gutmann, D.* ; Peters, A. ; Schlett, C.L.* ; Bamberg, F.* ; Wachinger, C.*

Deep shape analysis on abdominal organs for diabetes prediction.

Lect. Notes Comput. Sc. 11167 LNCS, 223-231 (2018)
Postprint DOI
Open Access Green
Morphological analysis of organs based on images is a key task in medical imaging computing. Several approaches have been proposed for the quantitative assessment of morphological changes, and they have been widely used for the analysis of the effects of aging, disease and other factors in organ morphology. In this work, we propose a deep neural network for predicting diabetes on abdominal shapes. The network directly operates on raw point clouds without requiring mesh processing or shape alignment. Instead of relying on hand-crafted shape descriptors, an optimal representation is learned in the end-to-end training stage of the network. For comparison, we extend the state-of-the-art shape descriptor BrainPrint to the AbdomenPrint. Our results demonstrate that the network learns shape representations that better separates healthy and diabetic individuals than traditional representations.
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Publication type Article: Journal article
Document type Scientific Article
Language english
Publication Year 2018
HGF-reported in Year 2018
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
ISBN 978-3-030-04746-7
Book Volume Title ShapeMI 2018: Shape in Medical Imaging
Journal Lecture Notes in Computer Science
Quellenangaben Volume: 11167 LNCS, Issue: , Pages: 223-231 Article Number: , Supplement: ,
Publisher Springer
Publishing Place Berlin [u.a.]
Institute(s) Institute of Epidemiology (EPI)
POF-Topic(s) 30202 - Environmental Health
Research field(s) Genetics and Epidemiology
PSP Element(s) G-504000-010
G-504000-001
DOI 10.1007/978-3-030-04747-4_21
Scopus ID 85057380560
Erfassungsdatum 2018-12-11
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