Automatic Airway Segmentation in Chest CT Using Convolutional Neural Networks

  • Antonio Garcia-Uceda JuarezEmail author
  • H. A. W. M. Tiddens
  • M. de BruijneEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11040)


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.


Airway 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.


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Biomedical Imaging Group Rotterdam, Departments of Medical Informatics and RadiologyErasmus MCRotterdamThe Netherlands
  2. 2.Department of Pediatric PulmonologyErasmus MC-Sophia Children HospitalRotterdamThe Netherlands
  3. 3.Department of Radiology and Nuclear MedicineErasmus MC-Sophia Children HospitalRotterdamThe Netherlands
  4. 4.Department of Computer ScienceUniversity of CopenhagenCopenhagenDenmark

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