title: Improving airway segmentation in computed tomography using leak detection with convolutional networks.
abstract: We propose a novel method to improve airway segmentation in thoracic computed tomography (CT) by detecting and removing leaks. Leak detection is formulated as a classification problem, in which a convolutional network (ConvNet) is trained in a supervised fashion to perform the classification task. In order to increase the segmented airway tree length, we take advantage of the fact that multiple segmentations can be extracted from a given airway segmentation algorithm by varying the parameters that influence the tree length and the amount of leaks. We propose a strategy in which the combination of these segmentations after removing leaks can increase the airway tree length while limiting the amount of leaks. This strategy therefore largely circumvents the need for parameter fine-tuning of a given airway segmentation algorithm. The ConvNet was trained and evaluated using a subset of inspiratory thoracic CT scans taken from the COPDGene study. Our method was validated on a separate independent set of the EXACT'09 challenge. We show that our method significantly improves the quality of a given leaky airway segmentation, achieving a higher sensitivity at a low false-positive rate compared to all the state-of-the-art methods that entered in EXACT09, and approaching the performance of the combination of all of them.
authors.affiliation: Diagnostic Image Analysis Group, Radboud University Medical Center, Geert Grooteplein 10, 6525 GA, Nijmegen, The Netherlands. Electronic address: jean-paul.charbonnier@radboudumc.nl.
authors.forename: Jean-Paul
authors.initials: JP
authors.lastname: Charbonnier
authors.affiliation: Diagnostic Image Analysis Group, Radboud University Medical Center, Geert Grooteplein 10, 6525 GA, Nijmegen, The Netherlands.
authors.forename: Eva M van
authors.initials: EMV
authors.lastname: Rikxoort
authors.affiliation: Diagnostic Image Analysis Group, Radboud University Medical Center, Geert Grooteplein 10, 6525 GA, Nijmegen, The Netherlands.
authors.forename: Arnaud A A
authors.initials: AAA
authors.lastname: Setio
authors.affiliation: Department of Radiology, Meander Medisch Centrum, Amersfoort, The Netherlands; Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands.
authors.forename: Cornelia M
authors.initials: CM
authors.lastname: Schaefer-Prokop
authors.affiliation: Diagnostic Image Analysis Group, Radboud University Medical Center, Geert Grooteplein 10, 6525 GA, Nijmegen, The Netherlands.
authors.forename: Bram van
authors.initials: BV
authors.lastname: Ginneken
authors.affiliation: Diagnostic Image Analysis Group, Radboud University Medical Center, Geert Grooteplein 10, 6525 GA, Nijmegen, The Netherlands.
authors.forename: Francesco
authors.initials: F
authors.lastname: Ciompi
mesh_terms: D000465:Algorithms; D006801:Humans; D015203:Reproducibility of Results; D012137:Respiratory System / Q000000981:diagnostic imaging*; D013909:Thorax / Q000000981:diagnostic imaging*; D014057:Tomography, X-Ray Computed / Q000379:methods*
keywords: Airway segmentation; Chest computed tomography; Convolutional networks