Multimodal MRI provides complementary and clinically relevant information to probe tissue condition and to characterize various diseases. However, it is often difficult to acquire sufficiently many modalities from the same subject due to limitations in study plans, while quantitative analysis is still demanded. In this work, we propose a unified conditional disentanglement framework to synthesize any arbitrary modality from an input modality. Our framework hinges on a cycle-constrained conditional adversarial training approach, where it can extract a modality-invariant anatomical feature with a modality-agnostic encoder and generate a target modality with a conditioned decoder. We validate our framework on four MRI modalities, including T1-weighted, T1 contrast enhanced, T2-weighted, and FLAIR MRI, from the BraTS'18 database, showing superior performance on synthesis quality over the comparison methods. In addition, we report results from experiments on a tumor segmentation task carried out with synthesized data.
@article{arxiv.2101.05434,
title = {A Unified Conditional Disentanglement Framework for Multimodal Brain MR Image Translation},
author = {Xiaofeng Liu and Fangxu Xing and Georges El Fakhri and Jonghye Woo},
journal= {arXiv preprint arXiv:2101.05434},
year = {2021}
}
Comments
Published in IEEE International Symposium on Biomedical Imaging (ISBI) 2021 for Oral presentation