Current deep learning based segmentation models often generalize poorly between domains due to insufficient training data. In real-world clinical applications, cross-domain image analysis tools are in high demand since medical images from different domains are often needed to achieve a precise diagnosis. An important example in radiology is generalizing from non-contrast CT to contrast enhanced CTs. Contrast enhanced CT scans at different phases are used to enhance certain pathologies or organs. Many existing cross-domain image-to-image translation models have been shown to improve cross-domain segmentation of large organs. However, such models lack the ability to preserve fine structures during the translation process, which is significant for many clinical applications, such as segmenting small calcified plaques in the aorta and pelvic arteries. In order to preserve fine structures during medical image translation, we propose a patch-based model using shared latent variables from a Gaussian mixture model. We compare our image translation framework to several state-of-the-art methods on cross-domain image translation and show our model does a better job preserving fine structures. The superior performance of our model is verified by performing two tasks with the translated images - detection and segmentation of aortic plaques and pancreas segmentation. We expect the utility of our framework will extend to other problems beyond segmentation due to the improved quality of the generated images and enhanced ability to preserve small structures.
Self-Consistent Recursive Diffusion Bridge for Medical Image Translation
Bilkent University（毕尔肯大学）；National Magnetic Resonance Research Center (UMRAM)（国家磁共振研究中心）；Stanford University（斯坦福大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Ambient-Pix2PixGAN for Translating Medical Images from Noisy Data
University of Michigan-Shanghai Jiao Tong University Joint Institute（密歇根大学-上海交通大学联合学院）；Global Institute of Future Technology, Shanghai Jiao Tong University（上海交通大学未来技术全球研究院）；School of Biomedical Engineering, Shanghai Jiao Tong University（上海交通大学生物医学工程学院）
pyMEAL: A Multi-Encoder Augmentation-Aware-Learning Toolbox for Robust Medical Image Translation
Hong Kong Centre for Cerebro-Cardiovascular Health Engineering (COCHE)（香港脑心血管健康工程中心）；Department of Informatics, Sokoine University of Agriculture（农业科学学院信息系）；Department of Diagnostic Radiology, The University of Hong Kong（香港大学诊断放射科）；State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong（香港大学脑科学与认知科学国家重点实验室）；Alzheimer’s Disease Research Network, The University of Hong Kong（香港大学阿尔茨海默病研究网络）；Department of Biomedical Engineering, City University of Hong Kong（城市大学生物医学工程系）；Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine（约翰霍普金斯大学医学院放射学与放射科学系）；City University of Hong Kong Shenzhen Research Institute（城市大学深圳研究 institute）
Multimodal Medical Image Binding via Shared Text Embeddings
The Hong Kong Polytechnic University（香港理工大学）；Emory University（埃默里大学）；The University of Hong Kong（香港大学）；University of Illinois Chicago（伊利诺伊大学芝加哥分校）；University of Science and Technology of China（中国科学技术大学）；Shanghai AI Laboratory（上海人工智能实验室）；Johns Hopkins University（约翰霍普金斯大学）
COSMOS: Cross-Modality Unsupervised Domain Adaptation for 3D Medical Image Segmentation based on Target-aware Domain Translation and Iterative Self-Training
Generative Artificial Intelligence in Medical Imaging: Foundations, Progress, and Clinical Translation
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Macao Polytechnic University（澳门理工大学）
Curvilinear Structure-preserving Unpaired Cross-domain Medical Image Translation
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore（南洋理工大学电子与电气工程学院）；Singapore Eye Research Institute, Singapore National Eye Centre, Singapore（新加坡眼科学研究所，新加坡国家眼科中心）；School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, China（武汉科技大学计算机科学与技术学院）；Ophthalmology & Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore（眼科与视觉科学学术临床计划，新加坡国立大学医学院）；Institute of Molecular and Clinical Ophthalmology, Basel, Switzerland（分子与临床眼科学研究所，瑞士巴塞尔）；School of Chemical and Biomedical Engineering, Nanyang Technological University, Singapore（南洋理工大学化学与生物医学工程学院）；Centre for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria（医学物理与生物医学工程中心，维也纳医科大学，维也纳，奥地利）；Department of Clinical Pharmacology, Medical University of Vienna, Vienna, Austria（临床药理学系，维也纳医科大学，维也纳，奥地利）；Rothschild Foundation Hospital, Paris, France（罗特希尔德基金会医院，法国巴黎）；Institute for Infocomm Research (I 2 R), A*STAR, Singapore（信息与通信研究所以（I 2 R），新加坡A*STAR）
Attentive Continuous Generative Self-training for Unsupervised Domain Adaptive Medical Image Translation
Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Johns Hopkins University（约翰斯·霍普金斯大学）；University of Maryland School of Dentistry（马里兰大学牙医学院）
Diffusion Bridge Models for 3D Medical Image Translation
University of California Riverside（加州大学河滨分校）；Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California（加州大学洛杉矶分校医学学院）；Information Sciences Institute, University of Southern California（加州大学洛杉矶分校信息科学研究所）
Cascaded Multi-path Shortcut Diffusion Model for Medical Image Translation
Yale University（耶鲁大学）；University of California Irvine（加州大学欧文分校）；University of Science and Technology of China（中国科学技术大学）；Case Western Reserve University（凯斯西储大学）；Northwestern University（西北大学）；Yale School of Medicine（耶鲁医学院）
Frequency Domain Decomposition Translation for Enhanced Medical Image Translation Using GANs
Institute of Medical Technology, Peking University Health Science Center（北京大学医学部医学技术研究院）；Biomedical Engineering Department, Peking University（北京大学生物医学工程系）；School of Electrical and Electronic Engineering, Nanyang Technological University（南洋理工大学电气与电子工程学院）；National Engineering Research Center of Oral Biomaterials and Digital Medical Devices（国家口腔生物材料与数字医疗器械工程研究中心）；NMPA Key Laboratory for Dental Materials（国家药监局牙科材料重点实验室）；Beijing Laboratory of Biomedical Materials（北京生物医学材料实验室）；Peking University School and Hospital of Stomatology（北京大学口腔医学院）；Department of Geriatric Dentistry, Peking University School and Hospital of Stomatology（北京大学口腔医学院老年口腔科）
Cross-conditioned Diffusion Model for Medical Image to Image Translation
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Xi’an Jiaotong University（西安交通大学）；The Hong Kong Polytechnic University（香港理工大学）；The Hong Kong University of Science and Technology（香港科技大学）
