Due to the cross-domain distribution shift aroused from diverse medical imaging systems, many deep learning segmentation methods fail to perform well on unseen data, which limits their real-world applicability. Recent works have shown the benefits of extracting domain-invariant representations on domain generalization. However, the interpretability of domain-invariant features remains a great challenge. To address this problem, we propose an interpretable Bayesian framework (BayeSeg) through Bayesian modeling of image and label statistics to enhance model generalizability for medical image segmentation. Specifically, we first decompose an image into a spatial-correlated variable and a spatial-variant variable, assigning hierarchical Bayesian priors to explicitly force them to model the domain-stable shape and domain-specific appearance information respectively. Then, we model the segmentation as a locally smooth variable only related to the shape. Finally, we develop a variational Bayesian framework to infer the posterior distributions of these explainable variables. The framework is implemented with neural networks, and thus is referred to as deep Bayesian segmentation. Quantitative and qualitative experimental results on prostate segmentation and cardiac segmentation tasks have shown the effectiveness of our proposed method. Moreover, we investigated the interpretability of BayeSeg by explaining the posteriors and analyzed certain factors that affect the generalization ability through further ablation studies. Our code will be released via https://zmiclab.github.io/projects.html, once the manuscript is accepted for publication.

翻译：摘要：由于不同医学成像系统引发的跨域分布偏移，许多深度学习分割方法在未知数据上表现不佳，这限制了其实际应用。近期研究已表明提取域不变表征对域泛化的益处。然而，域不变特征的可解释性仍是一大挑战。针对此问题，我们提出了一种可解释的贝叶斯框架（BayeSeg），通过对图像和标签统计量进行贝叶斯建模来增强医学图像分割的模型泛化能力。具体而言，我们首先将图像分解为空间相关变量和空间变异变量，通过分配分层贝叶斯先验显式地迫使这两类变量分别建模域稳定形状与域特定外观信息。接着，我们将分割建模为仅与形状相关的局部平滑变量。最后，我们开发了变分贝叶斯框架来推断这些可解释变量的后验分布。该框架通过神经网络实现，故称为深度贝叶斯分割。在前列腺分割和心脏分割任务上的定量与定性实验结果证明了所提方法的有效性。此外，我们通过解释后验分布探讨了BayeSeg的可解释性，并通过进一步的消融研究分析了影响泛化能力的某些因素。一旦手稿被接收发表，我们的代码将通过https://zmiclab.github.io/projects.html开源。