Saved in:
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2304.03708 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910561180057600 |
|---|---|
| author | Luo, Gongning Wang, Kuanquan Liu, Jun Li, Shuo Liang, Xinjie Li, Xiangyu Gan, Shaowei Wang, Wei Dong, Suyu Wang, Wenyi Yu, Pengxin Liu, Enyou Wei, Hongrong Wang, Na Guo, Jia Li, Huiqi Zhang, Zhao Zhao, Ziwei Gao, Na An, Nan Pakzad, Ashkan Rangelov, Bojidar Dou, Jiaqi Tian, Song Liu, Zeyu Wang, Yi Sivalingam, Ampatishan Punithakumar, Kumaradevan Qiu, Zhaowen Gao, Xin |
| author_facet | Luo, Gongning Wang, Kuanquan Liu, Jun Li, Shuo Liang, Xinjie Li, Xiangyu Gan, Shaowei Wang, Wei Dong, Suyu Wang, Wenyi Yu, Pengxin Liu, Enyou Wei, Hongrong Wang, Na Guo, Jia Li, Huiqi Zhang, Zhao Zhao, Ziwei Gao, Na An, Nan Pakzad, Ashkan Rangelov, Bojidar Dou, Jiaqi Tian, Song Liu, Zeyu Wang, Yi Sivalingam, Ampatishan Punithakumar, Kumaradevan Qiu, Zhaowen Gao, Xin |
| contents | 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/}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2304_03708 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Efficient automatic segmentation for multi-level pulmonary arteries: The PARSE challenge Luo, Gongning Wang, Kuanquan Liu, Jun Li, Shuo Liang, Xinjie Li, Xiangyu Gan, Shaowei Wang, Wei Dong, Suyu Wang, Wenyi Yu, Pengxin Liu, Enyou Wei, Hongrong Wang, Na Guo, Jia Li, Huiqi Zhang, Zhao Zhao, Ziwei Gao, Na An, Nan Pakzad, Ashkan Rangelov, Bojidar Dou, Jiaqi Tian, Song Liu, Zeyu Wang, Yi Sivalingam, Ampatishan Punithakumar, Kumaradevan Qiu, Zhaowen Gao, Xin Image and Video Processing Computer Vision and Pattern Recognition 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/}. |
| title | Efficient automatic segmentation for multi-level pulmonary arteries: The PARSE challenge |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2304.03708 |