Abstract
Detailed modeling of the airway tree from CT scan is important for 3D navigation involved in endobronchial intervention including for those patients infected with the novel coronavirus. Deep learning methods have the potential for automatic airway segmentation but require large annotated datasets for training, which is difficult for a small patient population and rare cases. Due to the unique attributes of noisy COVID-19 CTs (e.g., ground-glass opacity and consolidation), vanilla 3D Convolutional Neural Networks (CNNs) trained on clean CTs are difficult to be generalized to noisy CTs. In this work, a Collaborative Feature Disentanglement and Augmentation framework (CFDA) is proposed to harness the intrinsic topological knowledge of the airway tree from clean CTs incorporated with unique bias features extracted from the noisy CTs. Firstly, we utilize the clean CT scans and a small amount of labeled noisy CT scans to jointly acquire a bias-discriminative encoder. Feature-level augmentation is then designed to perform feature sharing and augmentation, which diversifies the training samples and increases the generalization ability. Detailed evaluation results on patient datasets demonstrated considerable improvements in the CFDA network. It has been shown that the proposed method achieves superior segmentation performance of airway in COVID-19 CTs against other state-of-the-art transfer learning methods.
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References
Chen, C., Dou, Q., Chen, H., Qin, J., Heng, P.A.: Unsupervised bidirectional cross-modality adaptation via deeply synergistic image and feature alignment for medical image segmentation. IEEE Trans. Med. Imaging 39(7), 2494–2505 (2020)
Chen, N., et al.: Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, china: a descriptive study. Lancet 395(10223), 507–513 (2020)
Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 424–432. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_49
Huang, X., Belongie, S.: Arbitrary style transfer in real-time with adaptive instance normalization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1501–1510 (2017)
Jin, D., Xu, Z., Harrison, A.P., George, K., Mollura, D.J.: 3D convolutional neural networks with graph refinement for airway segmentation using incomplete data labels. In: Wang, Q., Shi, Y., Suk, H.-I., Suzuki, K. (eds.) MLMI 2017. LNCS, vol. 10541, pp. 141–149. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67389-9_17
Jin, S., et al.: AI-assisted CT imaging analysis for COVID-19 screening: building and deploying a medical AI system in four weeks. MedRxiv (2020)
Garcia-Uceda Juarez, A., Tiddens, H.A.W.M., de Bruijne, M.: Automatic airway segmentation in chest CT using convolutional neural networks. In: Stoyanov, D., et al. (eds.) RAMBO/BIA/TIA -2018. LNCS, vol. 11040, pp. 238–250. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00946-5_24
Lo, P., et al.: Extraction of airways from CT (exact’09). IEEE Trans. Med. Imaging 31(11), 2093–2107 (2012)
Ma, J., et al.: Toward data-efficient learning: a benchmark for COVID-19 CT lung and infection segmentation. Med. Phys. 48(3), 1197–1210 (2021)
Mahmud, T., et al.: Covtanet: a hybrid tri-level attention-based network for lesion segmentation, diagnosis, and severity prediction of COVID-19 chest CT scans. IEEE Trans. Industr. Inf. 17(9), 6489–6498 (2020)
Nadeem, S.A., et al.: A CT-based automated algorithm for airway segmentation using freeze-and-grow propagation and deep learning. IEEE Trans. Med. Imaging 40(1), 405–418 (2020)
Qin, Y., et al.: Learning tubule-sensitive CNNS for pulmonary airway and artery-vein segmentation in CT. IEEE Trans. Med. Imaging 40(6), 1603–1617 (2021)
Rozantsev, A., Salzmann, M., Fua, P.: Beyond sharing weights for deep domain adaptation. IEEE Trans. Pattern Anal. Mach. Intell. 41(4), 801–814 (2018)
Schroff, F., Kalenichenko, D., Philbin, J.: Facenet: A unified embedding for face recognition and clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 815–823 (2015)
Shit, S., et al.: cLDice-a novel topology-preserving loss function for tubular structure segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 16560–16569 (2021)
Sulaiman, I., et al.: Microbial signatures in the lower airways of mechanically ventilated COVID-19 patients associated with poor clinical outcome. Nat. Microbiol. 6(10), 1245–1258 (2021)
Sun, B., Saenko, K.: Deep CORAL: correlation alignment for deep domain adaptation. In: Hua, G., Jégou, H. (eds.) ECCV 2016. LNCS, vol. 9915, pp. 443–450. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-49409-8_35
Wang, G., et al.: A noise-robust framework for automatic segmentation of COVID-19 pneumonia lesions from CT images. IEEE Trans. Med. Imaging 39(8), 2653–2663 (2020)
Wang, J., et al.: Prior-attention residual learning for more discriminative COVID-19 screening in CT images. IEEE Trans. Med. Imaging 39(8), 2572–2583 (2020)
Wang, Z., Liu, Q., Dou, Q.: Contrastive cross-site learning with redesigned net for COVID-19 CT classification. IEEE J. Biomed. Health Inform. 24(10), 2806–2813 (2020)
Wu, W., Wang, A., Liu, M., et al.: Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 395(10223), 497–506 (2020)
Xu, G.X., et al.: Cross-site severity assessment of COVID-19 from CT images via domain adaptation. IEEE Trans. Med. Imaging 41(1), 88–102 (2021)
Yu, W., Zheng, H., Zhang, M., Zhang, H., Sun, J., Yang, J.: BREAK: bronchi reconstruction by geodesic transformation and skeleton embedding. arXiv preprint arXiv:2202.00002 (2022)
Yun, J., et al.: Improvement of fully automated airway segmentation on volumetric computed tomographic images using a 2.5 dimensional convolutional neural net. Med. Image Anal. 51, 13–20 (2019)
Zhang, K., Liu, X., Shen, J., Li, Z., Sang, Y., Wu, X., Zha, Y., Liang, W., Wang, C., Wang, K., et al.: Clinically applicable AI system for accurate diagnosis, quantitative measurements, and prognosis of COVID-19 pneumonia using computed tomography. Cell 181(6), 1423–1433 (2020)
Zhang, M., et al.: FDA: feature decomposition and aggregation for robust airway segmentation. In: Albarqouni, S., et al. (eds.) DART/FAIR -2021. LNCS, vol. 12968, pp. 25–34. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87722-4_3
Zheng, H., et al.: Alleviating class-wise gradient imbalance for pulmonary airway segmentation. IEEE Trans. Med. Imaging 40(9), 2452–2462 (2021)
Zhu, W., et al.: AnatomyNet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Med. Phys. 46(2), 576–589 (2019)
Acknowledgement
This work is supported in part by the Open Funding of Zhejiang Laboratory under Grant 2021KH0AB03, in part by the Shanghai Sailing Program under Grant 20YF1420800, and in part by NSFC under Grant 62003208, and in part by Shanghai Municipal of Science and Technology Project, under Grant 20JC1419500 and Grant 20DZ2220400.
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Zhang, M., Zhang, H., Yang, GZ., Gu, Y. (2022). CFDA: Collaborative Feature Disentanglement and Augmentation for Pulmonary Airway Tree Modeling of COVID-19 CTs. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13431. Springer, Cham. https://doi.org/10.1007/978-3-031-16431-6_48
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DOI: https://doi.org/10.1007/978-3-031-16431-6_48
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