3D Brain and Heart Volume Generative Models: A Survey-Reference-Cited by-同舟云学术

3D Brain and Heart Volume Generative Models: A Survey

Published:2024-01-22 Issue:6 Volume:56 Page:1-37
ISSN:0360-0300
Container-title:ACM Computing Surveys
language:en
Short-container-title:ACM Comput. Surv.

Author:

Liu Yanbin¹^ORCID,Dwivedi Girish²^ORCID,Boussaid Farid³^ORCID,Bennamoun Mohammed⁴^ORCID

Affiliation:

1. Harry Perkins Institute of Medical Research, Department of Computer Science and Software Engineering, The University of Western Australia, Australia

2. Harry Perkins Institute of Medical Research, The University of Western Australia, Fiona Stanley Hospital, Australia

3. Department of Electrical, Electronic and Computer Engineering, The University of Western Australia, Australia

4. Department of Computer Science and Software Engineering, The University of Western Australia, Australia

Abstract

Generative models such as generative adversarial networks and autoencoders have gained a great deal of attention in the medical field due to their excellent data generation capability. This article provides a comprehensive survey of generative models for 3D volumes, focusing on the brain and heart. A new and elaborate taxonomy of unconditional and conditional generative models is proposed to cover diverse medical tasks for the brain and heart: unconditional synthesis, classification, conditional synthesis, segmentation, denoising, detection, and registration. We provide relevant background, examine each task, and also suggest potential future directions. A list of the latest publications will be updated on GitHub to keep up with the rapid influx of papers at https://github.com/csyanbin/3D-Medical-Generative-Survey .

Funder

MRFF Frontier Health and Medical Research

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science,Theoretical Computer Science

Link

https://dl.acm.org/doi/pdf/10.1145/3638044

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