Synthesis of Healthy Tissue Within Tumor Area via U-Net

This paper demonstrates our contributions to the task of ‘Synthesis (Local) - Inpainting, BraTS 2023 Challenge’. We propose a U-Net like model for synthesizing the healthy 3D brain tissue from the masked input with the aim to synthesize the healthy brain magnetic resonance imaging (MRI) scans from the pathological ones. To enhance our model’s generalizability and robustness, we work out a coherent strategy for data augmentation by generating randomly masked healthy images during the training phase. Our model is trained on the BraTS-Local-Inpainting training set and has achieved an overall performance with an SSIM score of 0.811946, a PSNR score of 21.445863 and an MSE score of 0.009317 on the BraTS-Local-Inpainting validation set computed by the online evaluation platform Synapse. Meanwhile, our model also has relatively low standard deviations for these three evaluation metrics, i.e. 0.113501 for SSIM score, 3.444001 for PSNR score and 0.006453 for MSE score. Our approach has ranked the first place in the testing phase on the outstanding performance with an SSIM score of 0.885162, a PSNR score of 23.849556, and an impressively low MSE score of 0.005523. The standard deviations for these three evaluation metrics in the test dataset are 0.102514 for SSIM score, 3.921114 for PSNR score, and 0.004766 for MSE score, respectively.