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

翻译：磁共振扫描的脑血管分割是诊断脑血管疾病的关键步骤。由于血管结构精细，手动血管分割耗时费力。因此，基于深度学习的自动分割技术受到广泛研究。传统深度学习模型复杂度高且缺乏决策可靠性指示，常被认为不可信。本研究首次将认知不确定性量化引入脑血管分割模型，旨在增强对深度学习模型的信任。通过构建结合贝叶斯近似与深度集成优势的高效集成模型，我们致力于克服传统概率网络的高计算成本问题。高模型不确定性区域与错误预测区域高度吻合，证明了该方法的有效性和可靠性。我们在分布外数据上对集成模型进行了大量实验，证明对于分布外图像，估计不确定性会显著增加。此外，剔除高不确定性区域可提升分割质量，这一结论对分布内与分布外数据均成立。该集成模型能以可靠方式解释其局限性，在分布外数据中仍能保持可信度，具备临床应用的潜力。