A myriad of algorithms for the automatic analysis of brain MR images is available to support clinicians in their decision-making. For brain tumor patients, the image acquisition time series typically starts with a scan that is already pathological. This poses problems, as many algorithms are designed to analyze healthy brains and provide no guarantees for images featuring lesions. Examples include but are not limited to algorithms for brain anatomy parcellation, tissue segmentation, and brain extraction. To solve this dilemma, we introduce the BraTS 2023 inpainting challenge. Here, the participants' task is to explore inpainting techniques to synthesize healthy brain scans from lesioned ones. The following manuscript contains the task formulation, dataset, and submission procedure. Later it will be updated to summarize the findings of the challenge. The challenge is organized as part of the BraTS 2023 challenge hosted at the MICCAI 2023 conference in Vancouver, Canada.

翻译：现有大量用于脑部磁共振图像的自动分析算法可供临床决策支持。针对脑肿瘤患者，其图像采集时间序列通常起始于已呈现病理特征的扫描图像。这一现象带来诸多难题，因为许多算法是为分析健康脑部而设计的，无法保证对带有病变区域图像的处理效果。具体案例包括但不限于脑解剖结构分割、组织切分及脑部提取等算法。为解决这一困境，我们提出BraTS 2023图像修复挑战。在此挑战中，参与者的任务是通过探索图像修复技术，从病变扫描图像中合成健康脑部扫描图像。本文档内容涵盖任务定义、数据集及提交流程，后续将更新以总结挑战成果。该挑战作为BraTS 2023挑战赛的组成部分，在加拿大温哥华举办的MICCAI 2023会议上组织。