用贝叶斯框架提升前列腺MRI分割的泛化能力
DeepBayesFlow: A Bayesian Structured Variational Framework for Generalizable Prostate Segmentation via Expressive Posteriors and SDE-Girsanov Uncertainty Modeling
- 通过可学习的归一化流建模复杂后验分布
- 在高维空间实现无共轭约束的灵活推断
- 结合随机微分方程与测度变换增强不确定性建模
自动前列腺MRI分割因患者间解剖差异、组织边界模糊及不同成像协议导致的数据分布偏移而面临持续挑战。为此，我们提出DeepBayesFlow，一种新型贝叶斯分割框架，旨在提升跨临床场景的鲁棒性与泛化能力。该框架引入三项关键创新：基于归一化流的可学习NF-后验模块，用于建模复杂且数据自适应的潜在分布；无需共轭假设的NCVI推断机制，支持高维空间中灵活的后验学习；以及基于随机微分方程与吉拉诺夫测度变换的SDE-Girsanov模块，通过连续时间扩散过程和形式化测度转换，提升潜在表示的时序一致性与物理合理的不确定性建模。上述组件协同工作，使模型既能捕捉域不变的结构先验，又能动态适应域特定变化，在异构前列腺MRI数据集上实现准确且可解释的分割。
原文摘要 · Abstract (English)
Automatic prostate MRI segmentation faces persistent challenges due to inter-patient anatomical variability, blurred tissue boundaries, and distribution shifts arising from diverse imaging protocols. To address these issues, we propose DeepBayesFlow, a novel Bayesian segmentation framework designed to enhance both robustness and generalization across clinical domains. DeepBayesFlow introduces three key innovations: a learnable NF-Posterior module based on normalizing flows that models complex, data-adaptive latent distributions; a NCVI inference mechanism that removes conjugacy constraints to enable flexible posterior learning in high-dimensional settings; and a SDE-Girsanov module that refines latent representations via time-continuous diffusion and formal measure transformation, injecting temporal coherence and physically grounded uncertainty into the inference process. Together, these components allow DeepBayesFlow to capture domain-invariant structural priors while dynamically adapting to domain-specific variations, achieving accurate and interpretable segmentation across heterogeneous prostate MRI datasets.
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