  Deep learning (DL) networks have recently been shown to outperform other
segmentation methods on various public, medical-image challenge datasets
[3,11,16], especially for large pathologies. However, in the context of
diseases such as Multiple Sclerosis (MS), monitoring all the focal lesions
visible on MRI sequences, even very small ones, is essential for disease
staging, prognosis, and evaluating treatment efficacy. Moreover, producing
deterministic outputs hinders DL adoption into clinical routines. Uncertainty
estimates for the predictions would permit subsequent revision by clinicians.
We present the first exploration of multiple uncertainty estimates based on
Monte Carlo (MC) dropout [4] in the context of deep networks for lesion
detection and segmentation in medical images. Specifically, we develop a 3D MS
lesion segmentation CNN, augmented to provide four different voxel-based
uncertainty measures based on MC dropout. We train the network on a
proprietary, large-scale, multi-site, multi-scanner, clinical MS dataset, and
compute lesion-wise uncertainties by accumulating evidence from voxel-wise
uncertainties within detected lesions. We analyze the performance of
voxel-based segmentation and lesion-level detection by choosing operating
points based on the uncertainty. Empirical evidence suggests that uncertainty
measures consistently allow us to choose superior operating points compared
only using the network's sigmoid output as a probability.
