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/}.
@article{arxiv.2304.03708,
title = {Efficient automatic segmentation for multi-level pulmonary arteries: The PARSE challenge},
author = {Gongning Luo and Kuanquan Wang and Jun Liu and Shuo Li and Xinjie Liang and Xiangyu Li and Shaowei Gan and Wei Wang and Suyu Dong and Wenyi Wang and Pengxin Yu and Enyou Liu and Hongrong Wei and Na Wang and Jia Guo and Huiqi Li and Zhao Zhang and Ziwei Zhao and Na Gao and Nan An and Ashkan Pakzad and Bojidar Rangelov and Jiaqi Dou and Song Tian and Zeyu Liu and Yi Wang and Ampatishan Sivalingam and Kumaradevan Punithakumar and Zhaowen Qiu and Xin Gao},
journal= {arXiv preprint arXiv:2304.03708},
year = {2024}
}