用扩散模型从CT图像预测中风患者病情发展和预后。
Stroke outcome and evolution prediction from CT brain using a spatiotemporal diffusion autoencoder
- 基于时空扩散自编码器，从CT图像生成语义丰富的中风表征。
- 在3573名患者数据上，预测次日病情严重度和出院功能结局表现最优。
- 适用于需要精准预判中风演化的临床场景，尤其适合医疗决策支持。
中风是全球范围内的主要死亡与残疾原因。准确预测病情发展和预后有望革新中风诊疗，实现个体化决策并改善治疗结果。然而，尽管神经影像学数据丰富，建模脑组织最终命运仍具挑战。本文将扩散概率模型的思想应用于颅脑CT图像，提出一种自监督的语义中风表征生成方法，并扩展至处理纵向影像及发病时间信息。在包含5,824张CT图像、来自3,573名患者的双中心数据集上验证，标签极少的情况下，该方法在预测次日病情严重程度及出院时的功能结局方面均达到最优性能。
原文摘要 · Abstract (English)
Stroke is a major cause of death and disability worldwide. Accurate outcome and evolution prediction has the potential to revolutionize stroke care by individualizing clinical decision-making leading to better outcomes. However, despite a plethora of attempts and the rich data provided by neuroimaging, modelling the ultimate fate of brain tissue remains a challenging task. In this work, we apply recent ideas in the field of diffusion probabilistic models to generate a self-supervised semantically meaningful stroke representation from Computed Tomography (CT) images. We then improve this representation by extending the method to accommodate longitudinal images and the time from stroke onset. The effectiveness of our approach is evaluated on a dataset consisting of 5,824 CT images from 3,573 patients across two medical centers with minimal labels. Comparative experiments show that our method achieves the best performance for predicting next-day severity and functional outcome at discharge.
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