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Semantic Image Synthesis for Abdominal CT

文献类型： 会议论文

第一作者： Yan Zhuang

作者： Yan Zhuang 1 ; Benjamin Hou 1 ; Tejas Sudharshan Mathai 1 ; Pritam Mukherjee 1 ; Boah Kim 1 ; Ronald M. Summers 1 ;

作者机构： 1.Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center

关键词： CT;Abdomen;Diffusion model;Semantic image synthesis

会议名称： MICCAI Workshop on Deep Generative Models for Medical Image Computing and Computer Assisted Intervention

主办单位：

页码： 214-224

摘要： As a new emerging and promising type of generative models, diffusion models have proven to outperform Generative Adversarial Networks (GANs) in multiple tasks, including image synthesis. In this work, we explore semantic image synthesis for abdominal CT using conditional diffusion models, which can be used for downstream applications such as data augmentation. We systematically evaluated the performance of three diffusion models, as well as to other state-of-the-art GAN-based approaches, and studied the different conditioning scenarios for the semantic mask. Experimental results demonstrated that diffusion models were able to synthesize abdominal CT images with better quality. Additionally, encoding the mask and the input separately is more effective than naive concatenating.

分类号： tp391.41-53

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