Generative adversarial networks (GANs) have provided promising data enrichment solutions by synthesizing high-fidelity images. However, generating large sets of labeled images with new anatomical variations remains unexplored. We propose a novel method for synthesizing cardiac magnetic resonance (CMR) images on a population of virtual subjects with a large anatomical variation, introduced using the 4D eXtended Cardiac and Torso (XCAT) computerized human phantom. We investigate two conditional image synthesis approaches grounded on a semantically-consistent mask-guided image generation technique: 4-class and 8-class XCAT-GANs. The 4-class technique relies on only the annotations of the heart; while the 8-class technique employs a predicted multi-tissue label map of the heart-surrounding organs and provides better guidance for our conditional image synthesis. For both techniques, we train our conditional XCAT-GAN with real images paired with corresponding labels and subsequently at the inference time, we substitute the labels with the XCAT derived ones. Therefore, the trained network accurately transfers the tissue-specific textures to the new label maps. By creating 33 virtual subjects of synthetic CMR images at the end-diastolic and end-systolic phases, we evaluate the usefulness of such data in the downstream cardiac cavity segmentation task under different augmentation strategies. Results demonstrate that even with only 20% of real images (40 volumes) seen during training, segmentation performance is retained with the addition of synthetic CMR images. Moreover, the improvement in utilizing synthetic images for augmenting the real data is evident through the reduction of Hausdorff distance up to 28% and an increase in the Dice score up to 5%, indicating a higher similarity to the ground truth in all dimensions.
TAI-GAN: A Temporally and Anatomically Informed Generative Adversarial Network for early-to-late frame conversion in dynamic cardiac PET inter-frame motion correction
Yale University（耶鲁大学）；IBM Research（IBM研究院）；Yale New Haven Hospital（耶鲁-纽黑文医院）；Yale School of Medicine（耶鲁医学院）
Synthetic Cardiac MRI Image Generation using Deep Generative Models
Department of Computer Engineering（计算机工程系）；University of Peradeniya（珀德尼亚大学）；University of Maryland（马里兰大学）；Department of Holistic Systems（整体系统系）；SimulaMet；Oslo, Norway（挪威奥斯陆）
Temporally Consistent and Controllable Video Generation of 2D Cine CMR via Latent Space Motion Modeling
Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences（苏州生物医学工程与技术研究所，中国科学院）；SyCoIA, IMT Mines Ales（SyCoIA，IMT Mines Ales）
Cardiac MRI Segmentation with Strong Anatomical Guarantees
University of Sherbrooke（舍布鲁克大学）；Université de Bourgogne Franche-Comté（勃艮第-弗朗什孔泰大学）；CArdiac Simulation & Imaging Software（心脏模拟与成像软件公司）；Université de Lyon（里昂大学）
EchoFlow: A Foundation Model for Cardiac Ultrasound Image and Video Generation
Imperial College London（帝国理工学院）；University of Birmingham（伯明翰大学）；Ultromics Ltd.（奥特罗米克斯有限公司）；University of Oxford（牛津大学）；Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）
Synthesis of Late Gadolinium Enhancement Images via Implicit Neural Representations for Cardiac Scar Segmentation
Amsterdam UMC, The Netherlands（阿姆斯特丹大学医学中心，荷兰）；University of Amsterdam, The Netherlands（阿姆斯特丹大学，荷兰）；Informatics Institute, University of Amsterdam, The Netherlands（阿姆斯特丹大学信息学院，荷兰）；Amsterdam Cardiovascular Sciences, Amsterdam UMC, The Netherlands（阿姆斯特丹心血管科学，阿姆斯特丹大学医学中心，荷兰）；Amsterdam Machine Learning Lab, University of Amsterdam, The Netherlands（阿姆斯特丹机器学习实验室，阿姆斯特丹大学，荷兰）；Department of Biomedical Engineering and Physics, Amsterdam UMC, The Netherlands（生物医学工程与物理系，阿姆斯特丹大学医学中心，荷兰）；Department of Experimental Cardiology, Amsterdam Cardiovascular Sciences, Heart Failure & Arrhythmias, Amsterdam UMC, The Netherlands（实验心脏病学系，阿姆斯特丹心血管科学，心力衰竭与心律失常，阿姆斯特丹大学医学中心，荷兰）；Department of Cardiology, Amsterdam UMC, The Netherlands（心脏病学系，阿姆斯特丹大学医学中心，荷兰）；Department of Radiology and Nuclear Medicine, Amsterdam UMC, The Netherlands（放射学与核医学系，阿姆斯特丹大学医学中心，荷兰）；Department of Radiology, Mayo Clinic, Rochester, United States of America（放射学系，梅奥诊所，罗切斯特，美国）
Anatomy-Guided Residual Motion Diffusion for Controllable 4D Cardiac MRI Synthesis
Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Science（中国科学院苏州生物医学工程技术研究所）；Systemes Complexes et Intelligence Artificielle, IMT Mines Ales（复杂系统与人工智能实验室，IMT 阿莱斯矿业学院）；School of Artificial Intelligence and Advanced Computing, Xi’an Jiaotong-Liverpool University（西交利物浦大学人工智能与先进计算学院）
Deformation-Recovery Diffusion Model (DRDM): Instance Deformation for Image Manipulation and Synthesis
The Kennedy Institute of Rheumatology, University of Oxford, U.K.；Chinese Academy of Medical Sciences Oxford Institute, University of Oxford, U.K.；Big Data Institute, University of Oxford, U.K.；edited MRC Laboratory of Medical Sciences, Imperial College London, U.K.；Department of Computer Science, University of Oxford, Oxford, U.K. [12.5pt] Project page: -5pt
Post-DAE: Anatomically Plausible Segmentation via Post-Processing with Denoising Autoencoders
Institute for Signals, Systems and Computational Intelligence, sinc(i) CONICET-UNL（信号、系统与计算智能研究所，sinc(i) 阿根廷国家科学与技术研究理事会-国立利托拉尔大学）；Imperial College London（伦敦帝国理工学院）
School of Computation, Information and Technology（计算信息学院）；Technical University of Munich（慕尼黑技术大学）；School of Medicine, Klinikum Rechts der Isar（医学院）；Department of Computer Science（计算机科学系）；University of Sheffield（谢菲尔德大学）；Department of Quantitative Biomedicine（定量生物医学系）；University of Zurich（苏黎世大学）；Department of Computing（计算系）；Imperial College London（伦敦帝国理工学院）；Department of Engineering Science（工程科学系）；University of Oxford（牛津大学）
Preserving Cardiac Integrity: A Topology-Infused Approach to Whole Heart Segmentation
Imperial College London（帝国理工学院）；Jilin university（吉林大学）；National Heart and Lung Institute, Imperial College London（帝国理工学院国家心肺研究所）；School of Biomedical Engineering & Imaging Sciences, King’s College London（伦敦国王学院生物医学工程与影像科学学院）；Cardiovascular Research Centre, Royal Brompton Hospital（皇家布朗普顿医院心血管研究中心）
