Bayesian Neural Networks for Uncertainty Estimation of Imaging Biomarkers

TL;DR

This paper introduces Bayesian neural networks to quantify segmentation uncertainty in medical imaging, improving the reliability of biomarker-based disease analysis.

Contribution

It proposes a framework to propagate segmentation uncertainty into statistical analysis, enhancing biomarker reliability in disease studies.

Findings

Bayesian neural networks effectively estimate segmentation uncertainty.

Incorporating uncertainty improves statistical inference accuracy.

Application to liver segmentation in diabetic patients shows clear benefits.

Abstract

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.