Cardiovascular medical image and analysis based on 3D vision: A comprehensive survey

With the rapid development of 3D vision and computer graphics technology, the way humans interact with the world has undergone significant transformations. 3D vision-related technologies have profoundly impacted the analysis of cardiovascular diseases (CVD) based on medical imaging diagnosis. In this paper, we provide a comprehensive review of CVD analysis based on 3D vision. First, we delineate cardiovascular imaging and cardiovascular data types from both medical and computational perspectives. Then, we introduce a systematic taxonomy to comprehensively review the current practices of 3D vision in cardiovascular applications, covering aspects such as 3D vascular segmentation, 3D vascular map generation, 3D vascular reconstruction, and 3D vascular super-resolution. Additionally, we compile a list of publicly accessible cardiac image datasets and code repositories to support the reproduction of related algorithms and foster data and algorithm sharing within the community. Finally, we discuss the inherent challenges and limitations of cardiovascular imaging methods based on 3D vision and their potential and propose directions for overcoming these obstacles in future research. • We present a comprehensive overview of 3D vision-based cardiovascular imaging, highlighting its revolutionary role, functionality, and limitations. • This survey explores future 3D vision applications in cardiovascular image analysis, focusing on integrating multimodal data for clinical diagnosis and prognosis. • This survey reviews state-of-the-art 3D vision methods for vascular tasks, including segmentation, graph generation, and super-resolution, while offering datasets and code repositories.