Advertisement

AirwayNet: A Voxel-Connectivity Aware Approach for Accurate Airway Segmentation Using Convolutional Neural Networks

  • Yulei Qin
  • Mingjian Chen
  • Hao Zheng
  • Yun Gu
  • Mali Shen
  • Jie YangEmail author
  • Xiaolin Huang
  • Yue-Min Zhu
  • Guang-Zhong Yang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11769)

Abstract

Airway segmentation on CT scans is critical for pulmonary disease diagnosis and endobronchial navigation. Manual extraction of airway requires strenuous efforts due to the complicated structure and various appearance of airway. For automatic airway extraction, convolutional neural networks (CNNs) based methods have recently become the state-of-the-art approach. However, there still remains a challenge for CNNs to perceive the tree-like pattern and comprehend the connectivity of airway. To address this, we propose a voxel-connectivity aware approach named AirwayNet for accurate airway segmentation. By connectivity modeling, conventional binary segmentation task is transformed into 26 tasks of connectivity prediction. Thus, our AirwayNet learns both airway structure and relationship between neighboring voxels. To take advantage of context knowledge, lung distance map and voxel coordinates are fed into AirwayNet as additional semantic information. Compared to existing approaches, AirwayNet achieved superior performance, demonstrating the effectiveness of the network’s awareness of voxel connectivity.

References

  1. 1.
    Charbonnier, J.P., et al.: Improving airway segmentation in computed tomography using leak detection with convolutional networks. MedIA 36, 52–60 (2017)Google Scholar
  2. 2.
    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_49CrossRefGoogle Scholar
  3. 3.
    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_17CrossRefGoogle Scholar
  4. 4.
    Juarez, A.G.-U., 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_24CrossRefGoogle Scholar
  5. 5.
    Kampffmeyer, M., Dong, N., Liang, X., Zhang, Y., Xing, E.P.: ConnNet: a long-range relation-aware pixel-connectivity network for salient segmentation. IEEE TIP 28(5), 2518–2529 (2019)MathSciNetGoogle Scholar
  6. 6.
    Lo, P., Sporring, J., Ashraf, H., Pedersen, J.J., de Bruijne, M.: Vessel-guided airway tree segmentation: a voxel classification approach. MedIA 14(4), 527–538 (2010)Google Scholar
  7. 7.
    Lo, P., et al.: Extraction of airways from CT (EXACT’09). IEEE TMI 31(11), 2093–2107 (2012)Google Scholar
  8. 8.
    Meng, Q., Roth, H.R., Kitasaka, T., Oda, M., Ueno, J., Mori, K.: Tracking and segmentation of the airways in chest CT using a fully convolutional network. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10434, pp. 198–207. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-66185-8_23CrossRefGoogle Scholar
  9. 9.
    Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-24574-4_28CrossRefGoogle Scholar
  10. 10.
    Van Rikxoort, E.M., Baggerman, W., van Ginneken, B.: Automatic segmentation of the airway tree from thoracic CT scans using a multi-threshold approach. In: Proceedings of Second International Workshop on Pulmonary Image Analysis, pp. 341–349 (2009)Google Scholar
  11. 11.
    Xu, Z., Bagci, U., Foster, B., Mansoor, A., Udupa, J.K., Mollura, D.J.: A hybrid method for airway segmentation and automated measurement of bronchial wall thickness on CT. MedIA 24(1), 1–17 (2015)Google Scholar
  12. 12.
    Yun, J., et al.: Improvement of fully automated airway segmentation on volumetric computed tomographic images using a 2.5 dimensional convolutional neural net. MedIA 51, 13–20 (2019)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Yulei Qin
    • 1
    • 2
  • Mingjian Chen
    • 1
    • 2
  • Hao Zheng
    • 1
    • 2
  • Yun Gu
    • 1
    • 2
  • Mali Shen
    • 4
  • Jie Yang
    • 1
    • 2
    Email author
  • Xiaolin Huang
    • 1
    • 2
  • Yue-Min Zhu
    • 3
  • Guang-Zhong Yang
    • 2
    • 4
  1. 1.Institute of Image Processing and Pattern RecognitionShanghai Jiao Tong UniversityShanghaiChina
  2. 2.Institute of Medical RoboticsShanghai Jiao Tong UniversityShanghaiChina
  3. 3.CREATIS (CNRS UMR 5220, INSERM U1206), INSA LyonLyonFrance
  4. 4.Hamlyn Centre for Robotic SurgeryImperial College LondonLondonUK

Personalised recommendations