Towards Reliable WMH Segmentation under Domain Shift: An Application Study using Maximum Entropy Regularization to Improve Uncertainty Estimation
Journal:
arXiv
Published Date:
Jun 17, 2025
Abstract
Accurate segmentation of white matter hyperintensities (WMH) is crucial for
clinical decision-making, particularly in the context of multiple sclerosis.
However, domain shifts, such as variations in MRI machine types or acquisition
parameters, pose significant challenges to model calibration and uncertainty
estimation. This study investigates the impact of domain shift on WMH
segmentation by proposing maximum-entropy regularization techniques to enhance
model calibration and uncertainty estimation, with the purpose of identifying
errors post-deployment using predictive uncertainty as a proxy measure that
does not require ground-truth labels. To do this, we conducted experiments
using a U-Net architecture to evaluate these regularization schemes on two
publicly available datasets, assessing performance with the Dice coefficient,
expected calibration error, and entropy-based uncertainty estimates. Our
results show that entropy-based uncertainty estimates can anticipate
segmentation errors, and that maximum-entropy regularization further
strengthens the correlation between uncertainty and segmentation performance
while also improving model calibration under domain shift.
