首次在脑血管分割中实现高效不确定性估计，提升模型可信度。
Efficient Epistemic Uncertainty Estimation in Cerebrovascular Segmentation
- 用贝叶斯近似与深度集成结合，降低计算开销。
- 未知数据上预测不确定性上升，且剔除高不确定区域可提分割质量。
- 适合临床应用，能可靠解释模型局限性。
MR扫描中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，人工分割耗时，因此基于深度学习（DL）的自动分割技术被广泛研究。然而，传统DL模型复杂度高且缺乏决策可靠性指示，常被认为不可信。本文首次将认知不确定性量化引入脑血管分割模型，以提升可信度。通过结合贝叶斯近似与深度集成的高效集成模型，克服了传统概率网络的高计算成本。实验表明，高不确定性区域与错误预测位置对齐，验证了方法的有效性与可靠性。在分布外（OOD）数据上，模型估计的不确定性显著上升；剔除高不确定性区域后，无论分布内或分布外数据的分割质量均得到改善。该模型能可靠解释自身局限性，适用于临床场景。
原文摘要 · Abstract (English)
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
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