Automatic Airway Segmentation in Chest CT Using Convolutional Neural Networks
Segmentation of the airway tree from chest computed tomography (CT) images is critical for quantitative assessment of airway diseases including bronchiectasis and chronic obstructive pulmonary disease (COPD). However, obtaining an accurate segmentation of airways from CT scans is difficult due to the high complexity of airway structures. Recently, deep convolutional neural networks (CNNs) have become the state-of-the-art for many segmentation tasks, and in particular the so-called Unet architecture for biomedical images. However, its application to the segmentation of airways still remains a challenging task. This work presents a simple but robust approach based on a 3D Unet to perform segmentation of airways from chest CTs. The method is trained on a dataset composed of 12 CTs, and tested on another 6 CTs. We evaluate the influence of different loss functions and data augmentation techniques, and reach an average dice coefficient of 0.8 between the ground-truth and our automated segmentations.
KeywordsAirway segmentation Convolutional neural networks Data augmentation Bronchiectasis CT
This work has been funded by the EU Innovative Medicines Initiative (IMI). We would like to thank F. Dubost for his help with the experiments and with writing of this manuscript. We would also like to thank F. Calvet for sharing with us his implementation of elastic image deformation.
- 2.Bauer, C., Bischof, H., Beichel, R.: Segmentation of airways based on gradient vector flow. In: Proceedings of 2nd International Workshop Pulmonary Image Analysis, pp. 191–201 (2009)Google Scholar
- 6.Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015)Google Scholar
- 7.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
- 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: Medical Image Computing and Computer-Assisted Intervention MICCAI 2017, pp. 198–207 (2017)Google Scholar
- 9.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
- 10.Milletari, F., Navab, N., Ahmadi, S.A.: V-Net: fully convolutional neural networks for volumetric medical image segmentation. In: 4th International Conference on 3D Vision (3DV) (2016)Google Scholar
- 12.Selvan, R., Welling, M., Pedersen, J.H., Petersen, J., de Bruijne, M.: Mean field network based graph refinement with application to airway tree extraction. arXiv preprint arXiv:1804.03348 (2018)
- 13.Baumgartner, C.F., Koch, L.M., Pollefeys, M., Konukoglu, E.: An exploration of 2D and 3D deep learning techniques for cardiac MR image segmentation. In: Pop, M., Sermesant, M., Jodoin, P.-M., Lalande, A., Zhuang, X., Yang, G., Young, A., Bernard, O. (eds.) STACOM 2017. LNCS, vol. 10663, pp. 111–119. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75541-0_12CrossRefGoogle Scholar
- 16.Krhenbhl, P., Koltun, V.: Efficient inference in Fully connected CRFs with Gaussian edge potentials. Adv. Neural Inf. Process. Syst. 24, 109–117 (2011)Google Scholar