Airway Tree Modeling (ATM'22) Benchmark

Official repository for MICCAI 2022 Challenge: Multi-site, Multi-Domain Airway Tree Modeling (ATM’22).

Minghui Zhang, Yangqian Wu, Hanxiao Zhang, Yulei Qin, Hao Zheng, Weihao Yu, Jiayuan Sun, Guang-Zhong Yang, Yun Gu.

ATM'22 organization team: Institute of Medical Robotics, Shanghai Jiao Tong University & Department of Respiratory and Critical Care Medicine, Shanghai Chest Hospital

Highlight: The benchmark manuscritpt: Multi-site, Multi-domain Airway Tree Modeling has been accepted for publication in Medical Image Analysis. If ATM'22 challenge, dataset or this repo is helpful to your scientific research, please cite the paper:

@article{zhang2023multi,
  title={Multi-site, Multi-domain Airway Tree Modeling},
  author={Zhang, Minghui and Wu, Yangqian and Zhang, Hanxiao and Qin, Yulei and Zheng, Hao and Tang, Wen and Arnold, Corey and Pei, Chenhao and Yu, Pengxin and Nan, Yang and others},
  journal={Medical Image Analysis},
  volume={90},
  pages={102957},
  year={2023},
  publisher={Elsevier}
}

Content

1. ATM'22 Challenge Collection
   • Registration
   • Baseline and Docker Tutorial
   • Evaluation
2. Citation and Dataset Rule
3. Related Works

ATM'22 Challenge Collection

This challenge is open-call (challenge opens for new submissions after MICCAI 2022 deadline). The online evaluation for individual algorithms is still working.

Registration

The registration information, and detailed information could refer to Registration Page.

NOTE: All participants must send the signed data agreement to IMR-ATM22@outlook.com for successful registration, as required in Registration Page.

Baseline and Docker Tutorial

We provide a baseline model and a detailed docker tutorial, please refer to: Baseline and Docker Example for detailed instructions.

Evaluation

The evaluation code is provided in Evaluation.

Citation and Dataset Rule

If you find this repo's papers, codes, and ATM'22 challenge data are helpful to your research, and if you use our dataset provided by ATM'22 for your scientific research, please cite the following works:

@inproceedings{zhang2022cfda,
  title={CFDA: Collaborative Feature Disentanglement and Augmentation for Pulmonary Airway Tree Modeling of COVID-19 CTs},
  author={Zhang, Minghui and Zhang, Hanxiao and Yang, Guang-Zhong and Gu, Yun},
  booktitle={Medical Image Computing and Computer Assisted Intervention--MICCAI 2022: 25th International Conference, Singapore, September 18--22, 2022, Proceedings, Part I},
  pages={506--516},
  year={2022},
  organization={Springer}
}

@article{zheng2021alleviating,
  title={Alleviating class-wise gradient imbalance for pulmonary airway segmentation},
  author={Zheng, Hao and Qin, Yulei and Gu, Yun and Xie, Fangfang and Yang, Jie and Sun, Jiayuan and Yang, Guang-Zhong},
  journal={IEEE Transactions on Medical Imaging},
  volume={40},
  number={9},
  pages={2452--2462},
  year={2021},
  publisher={IEEE}
}

@inproceedings{yu2022break,
  title={BREAK: Bronchi Reconstruction by gEodesic transformation And sKeleton embedding},
  author={Yu, Weihao and Zheng, Hao and Zhang, Minghui and Zhang, Hanxiao and Sun, Jiayuan and Yang, Jie},
  booktitle={2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)},
  pages={1--5},
  year={2022},
  organization={IEEE}
}

@inproceedings{qin2019airwaynet,
  title={Airwaynet: a voxel-connectivity aware approach for accurate airway segmentation using convolutional neural networks},
  author={Qin, Yulei and Chen, Mingjian and Zheng, Hao and Gu, Yun and Shen, Mali and Yang, Jie and Huang, Xiaolin and Zhu, Yue-Min and Yang, Guang-Zhong},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={212--220},
  year={2019},
  organization={Springer}
}

Alternatively, you could also cite our challenge benchmark manuscript:

@article{zhang2023multi,
  title={Multi-site, Multi-domain Airway Tree Modeling},
  author={Zhang, Minghui and Wu, Yangqian and Zhang, Hanxiao and Qin, Yulei and Zheng, Hao and Tang, Wen and Arnold, Corey and Pei, Chenhao and Yu, Pengxin and Nan, Yang and others},
  journal={Medical Image Analysis},
  volume={90},
  pages={102957},
  year={2023},
  publisher={Elsevier}
}

Related Works

We collected the papers related to pulmonary airway segmentation and bronchoscopy navigation:

Please refer to Related Works for detailed information.

Date	Author	Title	Conf/Jour	Code
2023	ATM'22 Organizers and Participants	Multi-site, Multi-domain Airway Tree Modeling	MedIA	Official
2023	Minghui Zhang	Towards Connectivity-Aware Pulmonary Airway Segmentation	IEEE JBHI	——
2023	Diedre S. Carmo	MEDPSeg: End-to-end segmentation of pulmonary structures and lesions in computed tomography	Arxiv	Official
2023	Yan Hu	Large-Kernel Attention Network with Distance Regression and Topological Self-correction for Airway Segmentation	AJCAI	——
2023	Ron Alterovitz	Landmark Based Bronchoscope Localization for Needle Insertion Under Respiratory Deformation	IROS	——
2023	Karen-Helene Støverud	AeroPath: An airway segmentation benchmark dataset with challenging pathology	Arxiv	Official
2023	Wehao Yu	AirwayFormer: Structure-Aware Boundary-Adaptive Transformers for Airway Anatomical Labeling	MICCAI	Official
2023	Difei Gu	Semi-Supervised Pulmonary Airway Segmentation with Two-Stage Feature Specialization Mechanism	ISBI	——
2023	Puyang Wang	Accurate Airway Tree Segmentation in CT Scans via Anatomy-aware Multi-class Segmentation and Topology-guided Iterative Learning	Arxiv	——
2023	Ziqiao Weng	Topology Repairing of Disconnected Pulmonary Airways and Vessels: Baselines and a Dataset	MICCAI	——
2023	Mingyue Zhao	GDDS: Pulmonary Bronchioles Segmentation with Group Deep Dense Supervision	Arxiv	——
2023	Hanxiao Zhang	Deep anatomy learning for lung airway and artery-vein segmentation with synthetic contrast-enhanced CT generation	IPCAI	——
2023	Yanan Wu	Two-stage Contextual Transformer-based Convolutional Neural Network for Airway Extraction from CT Images	Artificial Intelligence in Medicine	——
2022	Zeyu Tang	Human Treelike Tubular Structure Segmentation: A Comprehensive Review and Future Perspectives	CBM	——
2022	Shuai Chen	Label Refinement Network from Synthetic Error Augmentation for Medical Image Segmentation	IEEE TMI	Official
2023	Zeyu Tang	Adversarial Transformer for Repairing Human Airway Segmentation	IEEE JBHI	——
2022	Yang Nan	Fuzzy Attention Neural Network to Tackle Discontinuity in Airway Segmentation	IEEE TNNLS	Official
2022	Wehao Yu	TNN: Tree Neural Network for Airway Anatomical Labeling	IEEE TMI	Official
2022	Yun Gu	Vision-Kinematics-Interaction for Robotic-Assisted Bronchoscopy Navigation	IEEE TMI	——
2022	Minghui Zhang	CFDA: Collaborative Feature Disentanglement and Augmentation for Pulmonary Airway TreeModeling of COVID-19 CTs	MICCAI	Official
2022	Haifan Gong	BronchusNet: Region and Structure Prior Embedded Representation Learning for Bronchus Segmentation and Classification	Arxiv	——
2021	Wehao Yu	BREAK: Bronchi Reconstruction by gEodesic transformation And sKeleton embedding	ISBI	——
2021	Yangqian Wu	LTSP: long-term slice propagation for accurate airway segmentation	IJCARS	——
2021	Minghui Zhang	Fda: Feature decomposition and aggregation for robust airway segmentation	DART@MICCAI	——
2021	Hao Zheng	Refined Local-imbalance-based Weight for Airway Segmentation in CT	MICCAI	Official
2021	Hao Zheng	Alleviating class-wise gradient imbalance for pulmonary airway segmentation	IEEE TMI	Official
2021	A. Garcia-Uceda Juarez	Automatic airway segmentation from Computed Tomography using robust and efficient 3-D convolutional neural networks	Scientific Reports	Official
2020	Hanxiao Zhang	Pathological airway segmentation with cascaded neural networks for bronchoscopic navigation	IEEE ICRA	——
2020	Yulei Qin	Learning Tubule-Sensitive CNNs for Pulmonary Airway and Artery-Vein Segmentation in CT	IEEE TMI	Official
2020	Raghavendra Selvan	Graph refinement based airway extraction using mean-field networks and graph neural networks	MedIA	Official
2019	Jihye Yun	Improvement of fully automated airway segmentation on volumetric computed tomographic images using a 2.5 dimensional convolutional neural net	MedIA	——
2019	Chenglong Wang	Tubular structure segmentation using spatial fully connected network with radial distance loss for 3D medical images	MICCAI	——
2019	A. Garcia-Uceda Juarez	A joint 3D UNet-graph neural network-based method for airway segmentation from chest CTs	MLMI@MICCAI	——
2019	Yulei Qin	AirwayNet: A Voxel-Connectivity Aware Approach for Accurate Airway Segmentation Using Convolutional Neural Networks	MICCAI	——
2017	Qier Meng	Tracking and segmentation of the airways in chest CT using a fully convolutional network	MICCAI	——
2017	Jean-Paul Charbonnier	Improving airway segmentation in computed tomography using leak detection with convolutional networks	MedIA	——
2017	Dakai Jin	3D convolutional neural networks with graph refinement for airway segmentation using incomplete data labels	MLMI@MICCAI	——
2015	Ziyue Xu	A hybrid method for airway segmentation and automated measurement of bronchial wall thickness on CT	MedIA	——
2012	Pechin Lo	Extraction of airways from CT (EXACT'09)	IEEE TMI	——