Abstract
Purpose
Airway tree segmentation plays a pivotal role in chest computed tomography (CT) analysis tasks such as lesion localization, surgical planning, and intra-operative guidance. The remaining challenge is to identify small bronchi correctly, which facilitates further segmentation of the pulmonary anatomies.
Methods
A three-dimensional (3D) multi-scale feature aggregation network (MFA-Net) is proposed against the scale difference of substructures in airway tree segmentation. In this model, the multi-scale feature aggregation (MFA) block is used to capture the multi-scale context information, which improves the sensitivity of the small bronchi segmentation and addresses the local discontinuities. Meanwhile, the concept of airway tree partition is introduced to evaluate the segmentation performance at a more granular level.
Results
Experiments were conducted on a dataset of 250 CT scans, which were annotated by experienced clinical radiologists. Through the airway partition, we evaluated the segmentation results of the small bronchi compared with the state-of-the-art methods. Experiments show that MFA-Net achieves the best performance in the Dice similarity coefficient (DSC) in the intra-lobar airway and improves the true positive rate (TPR) by 7.59% on average. Besides, in the entire airway, the proposed method achieves the best results in DSC and TPR scores of 86.18% and 79.31%, respectively, with the consequence of higher false positives.
Conclusion
The MFA-Net is competitive with the state-of-the-art methods. The experiment results indicate that the MFA block improves the performance of the network by utilizing multi-scale context information. More accurate segmentation results will be more helpful in further clinical analysis.
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Acknowledgements
This work was supported by the National Major Science and Technology Projects of China under Grant 2018AAA0100201 and the Major Scientific and Technological Projects of the New Generation of Artificial Intelligence in Sichuan Province in 2018 under Grant 2018GZDZX0035.
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Zhou, K., Chen, N., Xu, X. et al. Automatic airway tree segmentation based on multi-scale context information. Int J CARS 16, 219–230 (2021). https://doi.org/10.1007/s11548-020-02293-x
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DOI: https://doi.org/10.1007/s11548-020-02293-x