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 pulmonary arteries (PA) in CTPA images plays a significant role in clinical applications. However%2C most existing methods concentrate only on main PA or branch PA segmentation separately and ignore segmentation efficiency. Besides%2C there is no public large-scale dataset focused on PA segmentation%2C which makes it highly challenging to compare the different methods. To benchmark multi-level PA segmentation algorithms%2C we organized the first textbf{P}ulmonary textbf{AR}tery textbf{SE}gmentation (PARSE) challenge. On the one hand%2C we focus on both the main PA and the branch PA segmentation. On the other hand%2C for better clinical application%2C 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:%2F%2Fparse2022.grand-challenge.org%2FParse2022%2F}.?quality=80&w=800)
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