title: Determination of lung segments in computed tomography images using the Euclidean distance to the pulmonary artery.
abstract: OBJECTIVE
  Computed tomography (CT) imaging is the modality of choice for lung cancer diagnostics. With the increasing number of lung interventions on sublobar level in recent years, determining and visualizing pulmonary segments in CT images and, in oncological cases, reliable segment-related information about the location of tumors has become increasingly desirable. Computer-assisted identification of lung segments in CT images is subject of this work.
  
  
  METHODS
  The authors present a new interactive approach for the segmentation of lung segments that uses the Euclidean distance of each point in the lung to the segmental branches of the pulmonary artery. The aim is to analyze the potential of the method. Detailed manual pulmonary artery segmentations are used to achieve the best possible segment approximation results. A detailed description of the method and its evaluation on 11 CT scans from clinical routine are given.
  
  
  RESULTS
  An accuracy of 2-3 mm is measured for the segment boundaries computed by the pulmonary artery-based method. On average, maximum deviations of 8 mm are observed. 135 intersegmental pulmonary veins detected in the 11 test CT scans serve as reference data. Furthermore, a comparison of the presented pulmonary artery-based approach to a similar approach that uses the Euclidean distance to the segmental branches of the bronchial tree is presented. It shows a significantly higher accuracy for the pulmonary artery-based approach in lung regions at least 30 mm distal to the lung hilum.
  
  
  CONCLUSIONS
  A pulmonary artery-based determination of lung segments in CT images is promising. In the tests, the pulmonary artery-based determination has been shown to be superior to the bronchial tree-based determination. The suitability of the segment approximation method for application in the planning of segment resections in clinical practice has already been verified in experimental cases. However, automation of the method accompanied by an evaluation on a larger number of test cases is required before application in the daily clinical routine.
authors.affiliation: Fraunhofer MEVIS, Institute for Medical Image Computing, Universitaetsallee 29, 28359 Bremen, Germany. christina.stoecker@mevis.fraunhofer.de
authors.forename: Christina
authors.initials: C
authors.lastname: Stoecker
authors.forename: Stefan
authors.initials: S
authors.lastname: Welter
authors.forename: Jan H
authors.initials: JH
authors.lastname: Moltz
authors.forename: Bianca
authors.initials: B
authors.lastname: Lassen
authors.forename: Jan-Martin
authors.initials: JM
authors.lastname: Kuhnigk
authors.forename: Stefan
authors.initials: S
authors.lastname: Krass
authors.forename: Heinz-Otto
authors.initials: HO
authors.lastname: Peitgen
mesh_terms: D000465:Algorithms; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008168:Lung / Q000098:blood supply* / Q000000981:diagnostic imaging*; D011651:Pulmonary Artery / Q000000981:diagnostic imaging*; D014057:Tomography, X-Ray Computed / Q000379:methods*