This paper presents the winning solution of task 1 and the third-placed solution of task 3 of the BraTS challenge. The use of automated tools in clinical practice has increased due to the development of more and more sophisticated and reliable algorithms. However, achieving clinical standards and developing tools for real-life scenarios is a major challenge. To this end, BraTS has organised tasks to find the most advanced solutions for specific purposes. In this paper, we propose the use of synthetic data to train state-of-the-art frameworks in order to improve the segmentation of adult gliomas in a post-treatment scenario, and the segmentation of meningioma for radiotherapy planning. Our results suggest that the use of synthetic data leads to more robust algorithms, although the synthetic data generation pipeline is not directly suited to the meningioma task. The code for these tasks is available at https://github.com/ShadowTwin41/BraTS_2023_2024_solutions.

本研究解决了临床脑肿瘤分割工具在实际应用中较难达到标准的问题。我们提出使用合成数据来训练先进的框架，以改善成人胶质瘤在后治疗场景下的分割效果，以及脑膜瘤的放疗计划分割。研究结果表明，合成数据的应用能够增强算法的鲁棒性，尽管生成管道并不完全适用于脑膜瘤任务。

改进的多任务脑肿瘤分割与合成数据增强