PuSH - Publikationsserver des Helmholtz Zentrums München

Export: TXT Text RIS Endnote (RIS) BIB BibTeX
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.
Impact Factor
Scopus SNIP
Altmetric
0.000
0.655
Tags
Anmerkungen
Besondere Publikation
Auf Hompepage verbergern

Zusatzinfos bearbeiten
Eigene Tags bearbeiten
Privat
Eigene Anmerkung bearbeiten
Privat
Auf Publikationslisten für
Homepage nicht anzeigen
Als besondere Publikation
markieren
Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Sprache englisch
Veröffentlichungsjahr 2018
HGF-Berichtsjahr 2018
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
ISBN 978-3-030-04746-7
Bandtitel ShapeMI 2018: Shape in Medical Imaging
Zeitschrift Lecture Notes in Computer Science
Quellenangaben Band: 11167 LNCS, Heft: , Seiten: 223-231 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]
Institut(e) Institute of Epidemiology (EPI)
POF Topic(s) 30202 - Environmental Health
Forschungsfeld(er) Genetics and Epidemiology
PSP-Element(e) G-504000-010
G-504000-001
DOI 10.1007/978-3-030-04747-4_21
Scopus ID 85057380560
Erfassungsdatum 2018-12-11
[➜Korrektur melden]