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 brain tumour segmentation, the first task of the BraTS2023
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. Using the new metric,
our best solution achieves the dice results 0.9005, 0.8673, 0.8509 and HD95
14.940, 14.467, 17.699 (whole tumour, tumour core and enhancing tumour) in the
validation set.

利用生成对抗网络、注册方法和深度学习模型在 BraTS2023 挑战赛的脑瘤分割任务中，通过非传统机制进行数据增广，实现大量可用样本进行训练，使用卷积算法和变换器填补彼此的知识差距，最佳解决方案在验证集上达到了 0.9005、0.8673、0.8509 的 Dice 结果和 14.940、14.467、17.699 的 HD95 结果（整个肿瘤、肿瘤核心和增强肿瘤）。