Synchronous Image-Label Diffusion with Anisotropic Noise for Stroke Lesion Segmentation on Non-Contrast CT

Automated segmentation of stroke lesions on non-contrast CT (NCCT) images is essential for efficient diagnosis of stroke patients. Although diffusion probabilistic models have shown promising advancements across various fields, their application to medical imaging exposes limitations due to the use of conventional isotropic Gaussian noise. Isotropic Gaussian noise overlooks the structural information and strong voxel dependencies in medical images. In this paper, a novel framework employing synchronous diffusion processes on image-labels is introduced, combined with a sampling strategy for anisotropic noise, to improve stroke lesion segmentation performance on NCCT. Our method acknowledges the significance of anatomical information during diffusion, contrasting with the traditional diffusion processes that assume isotropic Gaussian noise added to voxels independently. By integrating correlations among image voxels within specific anatomical regions into the denoising process, our approach enhances the robustness of neural networks, resulting in improved accuracy in stroke lesion segmentation. The proposed method has been evaluated on two datasets where experimental results demonstrate the capability of the proposed method to accurately segment ischemic infarcts on NCCT images. Furthermore, comparative analysis against state-of-the-art models, including U-net, transformer, and DPM-based segmentation methods, highlights the advantages of our method in terms of segmentation metrics. The code is publicly available at https://github.com/zhangjianhai/SADPM .