Abstract
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.
Abstract (translated)
许多用于自动分析大脑MRI图像的算法可供临床医生支持他们的决策。对于脑瘤患者，图像获取时间序列通常始于已经病理的扫描。这提出了问题，因为许多算法都是设计用于分析健康的大脑的，对于显示损伤的图像提供不了保证。例如，包括脑组织分割、组织分化和脑提取算法。为了解决这个困境，我们介绍了 BraTS 2023 填充挑战。在这里，参与者的任务是探索填充技术，从损伤的脑扫描中合成健康的脑扫描。以下手稿包含了任务 formulation、数据集和提交程序。稍后将更新以概括挑战的结果。挑战是 BraTS 2023 挑战在加拿大温哥华 MICCAI 2023 会议上举办的的一部分。
URL
https://arxiv.org/abs/2305.08992
