Stroke lesion volume is a key radiologic measurement for assessing the prognosis of Acute Ischemic Stroke (AIS) patients, which is challenging to be automatically measured on Non-Contrast CT (NCCT) scans. Recent diffusion probabilistic models have shown potentials of being used for image segmentation. In this paper, a novel Synchronous image-label Diffusion Probability Model (SDPM) is proposed for stroke lesion segmentation on NCCT using Markov diffusion process. The proposed SDPM is fully based on a Latent Variable Model (LVM), offering a complete probabilistic elaboration. An additional net-stream, parallel with a noise prediction stream, is introduced to obtain initial noisy label estimates for efficiently inferring the final labels. By optimizing the specified variational boundaries, the trained model can infer multiple label estimates for reference given the input images with noises. The proposed model was assessed on three stroke lesion datasets including one public and two private datasets. Compared to several U-net and transformer-based segmentation methods, our proposed SDPM model is able to achieve state-of-the-art performance. The code is publicly available.

翻译：卒中病灶体积是评估急性缺血性卒中（AIS）患者预后的关键影像学测量指标，但在非增强CT（NCCT）扫描中实现自动测量仍具挑战性。近年来，扩散概率模型在图像分割领域展现出应用潜力。本文提出一种基于马尔可夫扩散过程的同步图像-标签扩散概率模型（SDPM），用于NCCT图像中的卒中病灶分割。该模型完全基于潜变量模型（LVM），提供完整的概率化理论阐释。通过引入与噪声预测流并行的附加网络流，模型可获取初始噪声标签估计值，从而高效推断最终标签。通过优化指定的变分边界，训练后的模型能够针对输入含噪图像推断出多个参考标签估计值。所提模型在三个卒中病灶数据集（包括一个公开数据集和两个私有数据集）上进行了评估。与多种基于U-net和Transformer的分割方法相比，本文SDPM模型能够实现最优性能。相关代码已公开。