  Paired multi-modality medical images, can provide complementary information
to help physicians make more reasonable decisions than single modality medical
images. But they are difficult to generate due to multiple factors in practice
(e.g., time, cost, radiation dose). To address these problems, multi-modality
medical image translation has aroused increasing research interest recently.
However, the existing works mainly focus on translation effect of a whole image
instead of a critical target area or Region of Interest (ROI), e.g., organ and
so on. This leads to poor-quality translation of the localized target area
which becomes blurry, deformed or even with extra unreasonable textures. In
this paper, we propose a novel target-aware generative adversarial network
called TarGAN, which is a generic multi-modality medical image translation
model capable of (1) learning multi-modality medical image translation without
relying on paired data, (2) enhancing quality of target area generation with
the help of target area labels. The generator of TarGAN jointly learns mapping
at two levels simultaneously - whole image translation mapping and target area
translation mapping. These two mappings are interrelated through a proposed
crossing loss. The experiments on both quantitative measures and qualitative
evaluations demonstrate that TarGAN outperforms the state-of-the-art methods in
all cases. Subsequent segmentation task is conducted to demonstrate
effectiveness of synthetic images generated by TarGAN in a real-world
application. Our code is available at https://github.com/2165998/TarGAN.
