Efficient Epistemic Uncertainty Estimation in Cerebrovascular Segmentation
Journal:
arXiv
Published Date:
Mar 28, 2025
Abstract
Brain vessel segmentation of MR scans is a critical step in the diagnosis of
cerebrovascular diseases. Due to the fine vessel structure, manual vessel
segmentation is time consuming. Therefore, automatic deep learning (DL) based
segmentation techniques are intensively investigated. As conventional DL models
yield a high complexity and lack an indication of decision reliability, they
are often considered as not trustworthy. This work aims to increase trust in DL
based models by incorporating epistemic uncertainty quantification into
cerebrovascular segmentation models for the first time. By implementing an
efficient ensemble model combining the advantages of Bayesian Approximation and
Deep Ensembles, we aim to overcome the high computational costs of conventional
probabilistic networks. Areas of high model uncertainty and erroneous
predictions are aligned which demonstrates the effectiveness and reliability of
the approach. We perform extensive experiments applying the ensemble model on
out-of-distribution (OOD) data. We demonstrate that for OOD-images, the
estimated uncertainty increases. Additionally, omitting highly uncertain areas
improves the segmentation quality, both for in- and out-of-distribution data.
The ensemble model explains its limitations in a reliable manner and can
maintain trustworthiness also for OOD data and could be considered in clinical
applications
