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.

翻译：本文介绍了BraTS挑战赛任务1的获胜方案及任务3的第三名方案。随着算法日益精密可靠，自动化工具在临床实践中的应用日趋广泛。然而，达到临床标准并开发适用于真实场景的工具仍面临重大挑战。为此，BraTS组织专项任务以寻求针对特定目标的最先进解决方案。本文提出利用合成数据训练前沿框架，以改进治疗后场景下成人胶质瘤的分割效果，以及面向放疗规划的脑膜瘤分割精度。研究结果表明，尽管合成数据生成流程未直接适配脑膜瘤分割任务，但合成数据的使用能显著提升算法的鲁棒性。相关任务代码已发布于https://github.com/ShadowTwin41/BraTS_2023_2024_solutions。