Efficient automatic segmentation of multi-level (i.e. main and branch) pulmonary arteries (PA) in CTPA images plays a significant role in clinical applications. However, most existing methods concentrate only on main PA or branch PA segmentation separately and ignore segmentation efficiency. Besides, there is no public large-scale dataset focused on PA segmentation, which makes it highly challenging to compare the different methods. To benchmark multi-level PA segmentation algorithms, we organized the first \textbf{P}ulmonary \textbf{AR}tery \textbf{SE}gmentation (PARSE) challenge. On the one hand, we focus on both the main PA and the branch PA segmentation. On the other hand, for better clinical application, we assign the same score weight to segmentation efficiency (mainly running time and GPU memory consumption during inference) while ensuring PA segmentation accuracy. We present a summary of the top algorithms and offer some suggestions for efficient and accurate multi-level PA automatic segmentation. We provide the PARSE challenge as open-access for the community to benchmark future algorithm developments at \url{https://parse2022.grand-challenge.org/Parse2022/}.

翻译：在CTPA图像中实现多级（即主干与分支）肺动脉（PA）的高效自动分割在临床应用中具有重要意义。然而，现有方法大多仅单独关注主干PA或分支PA的分割，且忽视了分割效率。此外，目前缺乏专注于PA分割的公开大规模数据集，这使得不同方法之间的比较极具挑战性。为评估多级PA分割算法，我们组织了首届**肺动脉分割**（PARSE）挑战赛。一方面，我们同时关注主干PA与分支PA的分割；另一方面，为更好地服务临床应用，我们在保证PA分割精度的前提下，赋予分割效率（主要指推理过程中的运行时间与GPU内存消耗）同等的评分权重。本文总结了顶尖算法，并为高效精准的多级PA自动分割提出若干建议。我们将PARSE挑战赛作为开放资源提供给学术界，以便对未来算法发展进行基准测试，访问地址为：\url{https://parse2022.grand-challenge.org/Parse2022/}。