CausalDisenSeg: 一种基于因果推理的解耦框架，用于在缺失模态下的鲁棒脑肿瘤分割
CausalDisenSeg: A Causality-Guided Disentanglement Framework with Counterfactual Reasoning for Robust Brain Tumor Segmentation Under Missing Modalities
AI总结：
本文提出CausalDisenSeg框架，通过因果引导的解耦和反事实推理，解决多模态脑肿瘤分割中因模态偏差导致的鲁棒性问题，实验显示其在严重缺失模态场景下准确性和一致性均优于现有方法。
摘要：
在临床实践中，深度学习模型在多模态脑肿瘤分割中的鲁棒性严重受不完整MRI数据影响。这种脆弱性主要源于模态偏差，即模型利用虚假相关性作为捷径而非学习真实解剖结构。现有特征融合方法未能从根本上消除这种依赖。为此，我们提出了CausalDisenSeg，一种基于结构因果模型（SCM）的新型框架，通过因果引导的解耦和反事实推理实现稳健分割。我们将问题重新表述为隔离解剖因果因素与风格偏差因素。我们的框架实现了三阶段因果干预：（1）显式因果解耦：条件变分自动编码器（CVAE）结合HSIC约束数学上强制解剖和风格特征的统计正交性。（2）因果表征强化：区域因果模块（RCM）将因果特征明确地锚定在物理肿瘤区域。（3）反事实推理：双对抗策略主动抑制残余的自然直接效应（NDE）的偏差，迫使其空间注意力与因果路径互斥。在BraTS 2020数据集上的大量实验表明，CausalDisenSeg在严重缺失模态场景下准确性和一致性均显著优于现有方法。此外，在相同协议下对BraTS 2023进行跨数据集评估，获得状态领先的宏平均DSC为84.49。
英文摘要：
In clinical practice, the robustness of deep learning models for multimodal brain tumor segmentation is severely compromised by incomplete MRI data. This vulnerability stems primarily from modality bias, where models exploit spurious correlations as shortcuts rather than learning true anatomical structures. Existing feature fusion methods fail to fundamentally eliminate this dependency. To address this, we propose CausalDisenSeg, a novel Structural Causal Model (SCM)-grounded framework that achieves robust segmentation via causality-guided disentanglement and counterfactual reasoning. We reframe the problem as isolating the anatomical Causal Factor from the stylistic Bias Factor. Our framework implements a three-stage causal intervention: (1) Explicit Causal Disentanglement: A Conditional Variational Autoencoder (CVAE) coupled with an HSIC constraint mathematically enforces statistical orthogonality between anatomical and style features. (2) Causal Representation Reinforcement: A Region Causality Module (RCM) explicitly grounds causal features in physical tumor regions. (3) Counterfactual Reasoning: A dual-adversarial strategy actively suppresses the residual Natural Direct Effect (NDE) of the bias, forcing its spatial attention to be mutually exclusive from the causal path. Extensive experiments on the BraTS 2020 dataset demonstrate that CausalDisenSeg significantly outperforms state-of-the-art methods in accuracy and consistency across severe missing-modality scenarios. Furthermore, cross-dataset evaluation on BraTS 2023 under the same protocol yields a state-of-the-art macro-average DSC of 84.49.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
