Structure-Preserving Synthesis: MaskGAN for Unpaired MR-CT Translation
This addresses the need for accurate medical image synthesis without costly annotations, particularly for pediatric cases with rapid growth, though it is incremental as it builds on existing GAN-based methods.
The paper tackled the problem of inaccurate anatomical structure preservation in unpaired MR-CT image translation, especially with heavily misaligned pediatric scans, by proposing MaskGAN, which uses automatically extracted coarse masks to enforce structural consistency without expert annotations, achieving state-of-the-art performance on a challenging dataset.
Medical image synthesis is a challenging task due to the scarcity of paired data. Several methods have applied CycleGAN to leverage unpaired data, but they often generate inaccurate mappings that shift the anatomy. This problem is further exacerbated when the images from the source and target modalities are heavily misaligned. Recently, current methods have aimed to address this issue by incorporating a supplementary segmentation network. Unfortunately, this strategy requires costly and time-consuming pixel-level annotations. To overcome this problem, this paper proposes MaskGAN, a novel and cost-effective framework that enforces structural consistency by utilizing automatically extracted coarse masks. Our approach employs a mask generator to outline anatomical structures and a content generator to synthesize CT contents that align with these structures. Extensive experiments demonstrate that MaskGAN outperforms state-of-the-art synthesis methods on a challenging pediatric dataset, where MR and CT scans are heavily misaligned due to rapid growth in children. Specifically, MaskGAN excels in preserving anatomical structures without the need for expert annotations. The code for this paper can be found at https://github.com/HieuPhan33/MaskGAN.