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Senapati, J.* ; Roy, A.* ; Pölsterl, S.* ; Gutmann, D.* ; Gatidis, S.* ; Schlett, C.* ; Peters, A. ; Bamberg, F.* ; Wachinger, C.*

Bayesian neural networks for uncertainty estimation of imaging biomarkers.

Lect. Notes Comput. Sc. 12436 LNCS, 270-280 (2020)
Postprint DOI
Open Access Green
Image segmentation enables to extract quantitative measures from scans that can serve as imaging biomarkers for diseases. However, segmentation quality can vary substantially across scans, and therefore yield unfaithful estimates in the follow-up statistical analysis of biomarkers. The core problem is that segmentation and biomarker analysis are performed independently. We propose to propagate segmentation uncertainty to the statistical analysis to account for variations in segmentation confidence. To this end, we evaluate four Bayesian neural networks to sample from the posterior distribution and estimate the uncertainty. We then assign confidence measures to the biomarker and propose statistical models for its integration in group analysis and disease classification. Our results for segmenting the liver in patients with diabetes mellitus clearly demonstrate the improvement of integrating biomarker uncertainty in the statistical inference.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Zeitschrift Lecture Notes in Computer Science
Quellenangaben Band: 12436 LNCS, Heft: , Seiten: 270-280 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]
Nichtpatentliteratur Publikationen
Institut(e) Institute of Epidemiology II (EPI2)