计算机科学
翻译(生物学)
稳健性(进化)
人工智能
图像翻译
相似性(几何)
扩散
生成模型
扩散过程
计算机视觉
图像质量
医学影像学
频域
图像(数学)
噪音(视频)
模式识别(心理学)
算法
生成语法
物理
生物化学
化学
知识管理
创新扩散
信使核糖核酸
基因
热力学
作者
Yunxiang Li,Hua‐Chieh Shao,Xiao Liang,Liyuan Chen,Ruiqi Li,Steve B Jiang,Jing Wang,You Zhang
出处
期刊:Cornell University - arXiv
日期:2023-01-01
被引量:2
标识
DOI:10.48550/arxiv.2304.02742
摘要
Recently, the diffusion model has emerged as a superior generative model that can produce high quality and realistic images. However, for medical image translation, the existing diffusion models are deficient in accurately retaining structural information since the structure details of source domain images are lost during the forward diffusion process and cannot be fully recovered through learned reverse diffusion, while the integrity of anatomical structures is extremely important in medical images. For instance, errors in image translation may distort, shift, or even remove structures and tumors, leading to incorrect diagnosis and inadequate treatments. Training and conditioning diffusion models using paired source and target images with matching anatomy can help. However, such paired data are very difficult and costly to obtain, and may also reduce the robustness of the developed model to out-of-distribution testing data. We propose a frequency-guided diffusion model (FGDM) that employs frequency-domain filters to guide the diffusion model for structure-preserving image translation. Based on its design, FGDM allows zero-shot learning, as it can be trained solely on the data from the target domain, and used directly for source-to-target domain translation without any exposure to the source-domain data during training. We evaluated it on three cone-beam CT (CBCT)-to-CT translation tasks for different anatomical sites, and a cross-institutional MR imaging translation task. FGDM outperformed the state-of-the-art methods (GAN-based, VAE-based, and diffusion-based) in metrics of Frechet Inception Distance (FID), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index Measure (SSIM), showing its significant advantages in zero-shot medical image translation.
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