Learning a medical image segmentation model is an inherently ambiguous task, as uncertainties exist in both images (noise) and manual annotations (human errors and bias) used for model training. To build a trustworthy image segmentation model, it is important to not just evaluate its performance but also estimate the uncertainty of the model prediction. Most state-of-the-art image segmentation networks adopt a hierarchical encoder architecture, extracting image features at multiple resolution levels from fine to coarse. In this work, we leverage this hierarchical image representation and propose a simple yet effective method for estimating uncertainties at multiple levels. The multi-level uncertainties are modelled via the skip-connection module and then sampled to generate an uncertainty map for the predicted image segmentation. We demonstrate that a deep learning segmentation network such as U-net, when implemented with such hierarchical uncertainty estimation module, can achieve a high segmentation performance, while at the same time provide meaningful uncertainty maps that can be used for out-of-distribution detection.

翻译：学习一个医学图像分割模型本质上是一项具有模糊性的任务，因为图像（噪声）和用于模型训练的人工标注（人为误差和偏差）中都存在不确定性。为了构建可信的图像分割模型，不仅需要评估其性能，还需估计模型预测的不确定性。当前最先进的图像分割网络大多采用层次化的编码器架构，从精细到粗糙的多个分辨率层级提取图像特征。在本研究中，我们利用这种层次化的图像表示，提出了一种简单而有效的方法，用于在多个层级上估计不确定性。多层级不确定性通过跳跃连接模块进行建模，随后通过采样生成预测图像分割对应的不确定性图。我们证明，诸如U-net之类的深度学习分割网络，在应用这种层次化不确定性估计模块后，既能实现高分割性能，同时也能提供可用于分布外检测的有意义的不确定性图。