  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/}.
