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.

本文提出了一种基于扩散概率模型的非对比 CT 扫描中卒中病灶分割方法，该方法完全基于隐变量模型，在噪声预测流的帮助下能够高效地推断出最终标签，经多组数据集验证，证实其达到了最先进的性能。