Three-Dimensional Medical Image Synthesis with Denoising Diffusion Probabilistic Models
Three-Dimensional Medical Image Synthesis with Denoising Diffusion Probabilistic Models
复制标题
DOI：
--
复制
发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Zolnamar Dorjsembe;Furen Xiao
中科院分区：
文献类型：
--
作者：
Zolnamar Dorjsembe;Furen Xiao
消噪扩散概率模型（DDPM）近年来在图像合成中表现出优异的性能，并在各种图像处理任务中得到了广泛的研究。在这项工作中，我们提出了一个3D- ddpm来生成三维（3D）医学图像。与以往的研究不同，据我们所知，这项工作首次尝试研究DDPM以实现3D医学图像合成。我们的研究检查了脑肿瘤的高分辨率磁共振图像（MRI）的生成。通过在半公开数据集上的实验对所提出的方法进行了评估，定量和定性测试都显示出有希望的结果。我们的代码将在https://github.com/DL-Circle/3D-DDPM上公开提供
Denoising diffusion probabilistic models (DDPM) have recently shown superior performance in image synthesis and have been extensively studied in various image processing tasks. In this work, we propose a 3D-DDPM for generating three-dimensional (3D) medical images. Different from previous studies, to the best of our knowledge, this work presents the first attempt to investigate the DDPM to enable 3D medical image synthesis. Our study examined the generation of high-resolution magnetic resonance images (MRI) of brain tumors. The proposed method is evaluated through experiments on a semi-public dataset, with both quantitative and qualitative tests showing promising results. Our code will be publicly available at https://github.com/DL-Circle/3D-DDPM