A Survey of Cross-Modality Brain Image Synthesis [article]

Guoyang Xie, Jinbao Wang, Yawen Huang, Yefeng Zheng, Feng Zheng, Yaochu Jin
2022 arXiv   pre-print
The existence of completely aligned and paired multi-modal neuroimaging data has proved its effectiveness in diagnosis of brain diseases. However, collecting the full set of well-aligned and paired data is impractical or even luxurious, since the practical difficulties may include high cost, long time acquisition, image corruption, and privacy issues. A realistic solution is to explore either an unsupervised learning or a semi-supervised learning to synthesize the absent neuroimaging data. In
more » ... is paper, we tend to approach multi-modality brain image synthesis task from different perspectives, which include the level of supervision, the range of modality synthesis, and the synthesis-based downstream tasks. Particularly, we provide in-depth analysis on how cross-modality brain image synthesis can improve the performance of different downstream tasks. Finally, we evaluate the challenges and provide several open directions for this community. All resources are available at https://github.com/M-3LAB/awesome-multimodal-brain-image-systhesis
arXiv:2202.06997v2 fatcat:kqxte2xrcrcpjfkkhwrcxdjqsu