Towards Connectivity-Aware Pulmonary Airway Segmentation
By Minghui Zhang, Yun Gu
Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai
Detailed pulmonary airway segmentation is a clinically important task for endobronchial intervention and treatment of peripheral pulmonary lesions. Breakage of small bronchi distals cannot be effectively eliminated in the prediction results of CNNs, which is detrimental to use as a reference for bronchoscopic-assisted surgery. We proposed a connectivity-aware segmentation method to improve the performance of airway segmentation. A Connectivity-Aware Surrogate (CAS) module is first proposed to balance the training progress within-class distribution. Furthermore, a Local-Sensitive Distance (LSD) module is designed to identify the breakage and minimize the variation of the distance map between the prediction and ground-truth.
To quick start, we provided the pretained networks, and can try the script in tests/_test_airway_model
python _test_airway_model.py
You can download our pretrained checkpoint from here. The configs and models are specified in
configs/airway_config and networks/airway_network.
The implementation of two modules CAS and LSD is modularized in the networks/CAS,networks/LSD.
If you find this repository or our paper useful, please consider citing our paper:
@article{zhang2023towards,
title={Towards Connectivity-Aware Pulmonary Airway Segmentation},
author={Zhang, Minghui and Gu, Yun},
journal={IEEE Journal of Biomedical and Health Informatics},
year={2023},
publisher={IEEE}
}