A Unified Conditional Disentanglement Framework for Multimodal Brain MR Image Translation
This work addresses the challenge of acquiring multiple MRI modalities for medical analysis, which is incremental as it builds on existing image translation techniques.
The authors tackled the problem of synthesizing missing MRI modalities from available ones using a unified conditional disentanglement framework, achieving superior synthesis quality on four MRI modalities from the BraTS'18 database compared to existing methods.
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.