后验采样是医学图像翻译中缺失模态的生成器
Posterior Samplings are Missing Modalities Generators for Medical Image Translation
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中文总结 AI 辅助
针对医学图像因时间和协议限制存在缺失模态的问题，提出统一框架，将缺失模态生成视为联合分布下的线性逆问题，通过后验采样及流匹配模型解决，在多数据集实验中性能优于基线，下游肿瘤分割表现更好。
中文摘要 AI 辅助
磁共振成像有多种模态对比，能提供互补的解剖和病理信息。由于时间和协议限制，完整的多模态采集通常不可用，导致实际数据集存在缺失模态。传统医学图像翻译方法通常限于固定的源 - 目标设置，或需为每个观察到的源 - 目标对重新训练。我们提出一个统一框架，将缺失模态生成公式化为联合分布下的线性逆问题，并通过带有流匹配模型的后验采样来解决。通过学习完整模态集上的联合先验，我们的方法可在推理时通过引导采样轨迹来重建任意缺失模态，以确保与观察到的模态测量一致。我们还采用多对一采样策略减轻多目标生成中的模态间误差传播。在BraTS和IXI数据集上的实验表明，我们的方法在大多数缺失模态场景下比基线有更好的性能。在下游肿瘤分割中，我们方法合成的图像有更高的分割性能，表明更好地保留了临床相关结构。
英文摘要
Magnetic resonance imaging comes in various modality contrasts that provide complementary anatomical and pathological information. Complete multimodal acquisitions are often unavailable due to time and protocol constraints. This leads to real-world datasets with missing modalities, where conventional medical image translation methods are typically limited to fixed source-target settings or require retraining for each observed source-target pair. We propose a unified framework that formulates missing-modality generation as a linear inverse problem under a joint distribution and solves it via posterior sampling with a flow matching model. By learning a joint prior over the complete modality set, our method can reconstruct arbitrary missing modalities at inference time by guiding the sampling trajectory to enforce measurement consistency with observed modalities. We further mitigate inter-modality error propagation in multi-target generation by adopting a many-to-one sampling strategy. Experiments on BraTS and IXI datasets show that our method achieves the best performance over baselines across most missing-modality scenarios. In downstream tumor segmentation, synthesized images from our method result in higher segmentation performance, indicating better preservation of clinically relevant structures.
发表机构
- Sungkyunkwan University（成均馆大学）
机构由 AI 辅助整理，请以论文原文为准。
