Enhanced Data Augmentation Using Synthetic Data for Brain Tumour Segmentation

Deep Learning is the state-of-the-art technology for segmenting brain tumours. However, this requires a lot of high-quality data, which is difficult to obtain, especially in the medical field. Therefore, our solutions address this problem by using unconventional mechanisms for data augmentation. Generative adversarial networks and registration are used to massively increase the amount of available samples for training three different deep learning models for adult glioma segmentation, the Task 1 of the BraTS 2023 challenge. The first model is the standard nnU-Net, the second is the Swin UNETR and the third is the winning solution of the BraTS 2021 Challenge. The entire pipeline is built on the nnU-Net implementation, except for the generation of the synthetic data. The use of convolutional algorithms and transformers is able to fill each other’s knowledge gaps. Our solution achieves the lesion-wise Dice metric of 0.8851, 0.8719, 0.8685 and lesion-wise HD95 of 22.838, 22.974, 16.713 (whole tumour, tumour core and enhancing tumour) on the testing cohort.