PuSH - Publikationsserver des Helmholtz Zentrums München

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)
Preprint DOI Verlagsversion bestellen
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
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.
Weitere Metriken?
Zusatzinfos bearbeiten [➜Einloggen]
Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
ISBN 978-3-030-04746-7
Bandtitel ShapeMI 2018: Shape in Medical Imaging
Quellenangaben Band: 11167 LNCS, Heft: , Seiten: 223-231 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]