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.

翻译：[医学图像合成因配对数据稀缺而颇具挑战性。现有方法虽能利用CycleGAN处理非配对数据，但常产生导致解剖结构偏移的不精确映射。当源模态与目标模态图像严重错位时，该问题尤为突出。近期方法试图通过引入辅助分割网络来缓解此问题，但该策略需要昂贵且耗时的像素级标注。为克服这一困境，本文提出MaskGAN——一种新颖且经济高效的框架，通过利用自动提取的粗略掩膜强制保持结构一致性。该方法采用掩膜生成器勾勒解剖结构，并配合内容生成器合成与这些结构对齐的CT影像。大量实验表明，在儿童快速生长导致MRI与CT扫描严重错位的儿科数据集上，MaskGAN优于当前最先进的合成方法。具体而言，MaskGAN无需专家标注即可出色地保留解剖结构。论文代码参见https://github.com/HieuPhan33/MaskGAN。]