Hematoma expansion (HE) after spontaneous intracerebral hemorrhage (ICH) is a major determinant of acute triage and treatment decisions in neurosurgical care. However, most existing methods provide either a binary expansion risk or a single follow-up volume, limiting uncertainty-aware decisions. We introduce HemExp, a clinically-guided latent diffusion model that generates patient-specific follow-up non-contrast CT images, along with segmentations of intraparenchymal and intraventricular hemorrhage. Generation is conditioned on baseline imaging, clinical variables, and an explicit expansion indicator, enabling controllable simulation of realistic clinical scenarios. HemExp uses a hemorrhage-aware multi-head variational autoencoder and models progression as the difference between baseline and follow-up latent representations with a conditional diffusion model. The model is trained on paired scans from 450 patients across multiple centers and evaluated on 107 patients from a held-out institution. HemExp produces spatial HE probability maps by generating multiple synthetic follow-up images per patient to estimate distributions of plausible follow-up hematoma volumes. Perturbing clinical inputs such as symptom-onset-to-imaging time or anticoagulant status shifts the predicted follow-up volume distribution. HemExp extends binary predictors and demonstrates robust estimation of clinically relevant outcomes in the imaging space, such as hematoma volume, intraventricular involvement, and mass effects. Overall, our results support controllable latent diffusion as a promising direction for uncertainty-aware modeling of early ICH progression.

翻译：摘要：自发性脑出血（ICH）后的血肿扩张（HE）是神经外科诊疗中急性分诊和治疗决策的主要决定因素。然而，现有方法大多仅提供二分类扩张风险或单一随访血肿体积预测，限制了不确定性的感知决策。我们提出HemExp——一种临床引导的潜空间扩散模型，可生成患者特异性的随访非增强CT图像，以及脑实质内和脑室内出血的分割结果。该生成过程以基线影像、临床变量和显式扩张指标为条件，实现对真实临床场景的可控模拟。HemExp采用血肿感知多头变分自编码器，并利用条件扩散模型将疾病进展建模为基线与随访潜空间表示之间的差异。该模型基于多中心450例患者的配对扫描数据进行训练，并在来自独立机构的107例患者上评估。HemExp通过为每例患者生成多张合成随访图像，估计可能的随访血肿体积分布，从而生成空间血肿扩张概率图。扰动临床输入（如发病至影像时间或抗凝状态）会改变预测的随访血肿体积分布。HemExp拓展了二分类预测器，并在影像空间中展现出对临床相关结局（如血肿体积、脑室受累及占位效应）的稳健估计能力。总体而言，我们的研究结果表明，可控潜空间扩散是进行早期脑出血进展不确定性感知建模的一个有前景的方向。