Structural-Based Uncertainty in Deep Learning Across Anatomical Scales:
  Analysis in White Matter Lesion Segmentation
Nataliia Molchanova Vatsal Raina Andrey Malinin Francesco La Rosa Adrien Depeursinge Mark Gales Cristina Granziera Henning Muller Mara Graziani Meritxell Bach Cuadra
This paper explores uncertainty quantification (UQ) as an indicator of the
trustworthiness of automated deep-learning (DL) tools in the context of white
matter lesion (WML) segmentation from magnetic resonance imaging (MRI) scans of
multiple sclerosis (MS) patients. Our study focuses on two principal aspects of
uncertainty in structured output segmentation tasks. Firstly, we postulate that
a good uncertainty measure should indicate predictions likely to be incorrect
with high uncertainty values. Second, we investigate the merit of quantifying
uncertainty at different anatomical scales (voxel, lesion, or patient). We
hypothesize that uncertainty at each scale is related to specific types of
errors. Our study aims to confirm this relationship by conducting separate
analyses for in-domain and out-of-domain settings. Our primary methodological
contributions are (i) the development of novel measures for quantifying
uncertainty at lesion and patient scales, derived from structural prediction
discrepancies, and (ii) the extension of an error retention curve analysis
framework to facilitate the evaluation of UQ performance at both lesion and
patient scales. The results from a multi-centric MRI dataset of 172 patients
demonstrate that our proposed measures more effectively capture model errors at
the lesion and patient scales compared to measures that average voxel-scale
uncertainty values. We provide the UQ protocols code at
https://github.com/Medical-Image-Analysis-Laboratory/MS_WML_uncs.