High Tissue Contrast MRI Synthesis Using Multi-Stage Attention-GAN for Segmentation


  • Mohammad Hamghalam Shenzhen University
  • Baiying Lei Shenzhen University
  • Tianfu Wang Shenzhen University




Magnetic resonance imaging (MRI) provides varying tissue contrast images of internal organs based on a strong magnetic field. Despite the non-invasive advantage of MRI in frequent imaging, the low contrast MR images in the target area make tissue segmentation a challenging problem. This paper demonstrates the potential benefits of image-to-image translation techniques to generate synthetic high tissue contrast (HTC) images. Notably, we adopt a new cycle generative adversarial network (CycleGAN) with an attention mechanism to increase the contrast within underlying tissues. The attention block, as well as training on HTC images, guides our model to converge on certain tissues. To increase the resolution of HTC images, we employ multi-stage architecture to focus on one particular tissue as a foreground and filter out the irrelevant background in each stage. This multi-stage structure also alleviates the common artifacts of the synthetic images by decreasing the gap between source and target domains. We show the application of our method for synthesizing HTC images on brain MR scans, including glioma tumor. We also employ HTC MR images in both the end-to-end and two-stage segmentation structure to confirm the effectiveness of these images. The experiments over three competitive segmentation baselines on BraTS 2018 dataset indicate that incorporating the synthetic HTC images in the multi-modal segmentation framework improves the average Dice scores 0.8%, 0.6%, and 0.5% on the whole tumor, tumor core, and enhancing tumor, respectively, while eliminating one real MRI sequence from the segmentation procedure.




How to Cite

Hamghalam, M., Lei, B., & Wang, T. (2020). High Tissue Contrast MRI Synthesis Using Multi-Stage Attention-GAN for Segmentation. Proceedings of the AAAI Conference on Artificial Intelligence, 34(04), 4067-4074. https://doi.org/10.1609/aaai.v34i04.5825



AAAI Technical Track: Machine Learning