  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.
