Cross-modality Neuroimage Synthesis: A Survey

Author:

Xie Guoyang1ORCID,Huang Yawen2ORCID,Wang Jinbao3ORCID,Lyu Jiayi4ORCID,Zheng Feng3ORCID,Zheng Yefeng2ORCID,Jin Yaochu5ORCID

Affiliation:

1. Southern University of Science and Technology, China and University of Surrey, United Kingdom

2. Jarvis Research Center, Tencent YouTu Lab, China

3. Southern University of Science and Technology, China

4. University of Chinese Academy of Sciences, China

5. Westlake University, China and University of Surrey, United Kingdom

Abstract

Multi-modality imaging improves disease diagnosis and reveals distinct deviations in tissues with anatomical properties. The existence of completely aligned and paired multi-modality neuroimaging data has proved its effectiveness in brain research. However, collecting fully aligned and paired data is expensive or even impractical, since it faces many difficulties, including high cost, long acquisition time, image corruption, and privacy issues. An alternative solution is to explore unsupervised or weakly supervised learning methods to synthesize the absent neuroimaging data. In this article, we provide a comprehensive review of cross-modality synthesis for neuroimages, from the perspectives of weakly supervised and unsupervised settings, loss functions, evaluation metrics, imaging modalities, datasets, and downstream applications based on synthesis. We begin by highlighting several opening challenges for cross-modality neuroimage synthesis. Then, we discuss representative architectures of cross-modality synthesis methods under different supervisions. This is followed by a stepwise in-depth analysis to evaluate how cross-modality neuroimage synthesis improves the performance of its downstream tasks. Finally, we summarize the existing research findings and point out future research directions. All resources are available at https://github.com/M-3LAB/awesome-multimodal-brain-image-systhesis.

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science,Theoretical Computer Science

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