title: A method for the automatic quantification of the completeness of pulmonary fissures: evaluation in a database of subjects with severe emphysema.
abstract: OBJECTIVE
  To propose and evaluate a technique for automatic quantification of fissural completeness from chest computed tomography (CT) in a database of subjects with severe emphysema.
  
  
  METHODS
  Ninety-six CT studies of patients with severe emphysema were included. The lungs, fissures and lobes were automatically segmented. The completeness of the fissures was calculated as the percentage of the lobar border defined by a fissure. The completeness score of the automatic method was compared with a visual consensus read by three radiologists using boxplots, rank sum tests and ROC analysis.
  
  
  RESULTS
  The consensus read found 49% (47/96), 15% (14/96) and 67% (64/96) of the right major, right minor and left major fissures to be complete. For all fissures visually assessed as being complete the automatic method resulted in significantly higher completeness scores (mean 92.78%) than for those assessed as being partial or absent (mean 77.16%; all p values <0.001). The areas under the curves for the automatic fissural completeness were 0.88, 0.91 and 0.83 for the right major, right minor and left major fissures respectively.
  
  
  CONCLUSIONS
  An automatic method is able to quantify fissural completeness in a cohort of subjects with severe emphysema consistent with a visual consensus read of three radiologists.
  
  
  CONCLUSIONS
  • Lobar fissures are important for assessing the extent and distribution of lung disease                                                  • Modern CT allows automatic lobar segmentation and assessment of the fissures                                                  • This segmentation can also assess the completeness of the fissures.                                                  • Such assessment is important for decisions about novel therapies (eg for emphysema).
authors: Center for Computer Vision and Imaging Biomarkers and Thoracic Imaging Research Group, Department of Radiological Sciences, David Geffen School of Medicine, University of California-Los Angeles, 924 Westwood Blvd, suite 650, Los Angeles, CA 90024, USA. e.vanrikxoort@rad.umcn.nl
authors: Eva M
authors: EM
authors: van Rikxoort
authors: Jonathan G
authors: JG
authors: Goldin
authors: Maya
authors: M
authors: Galperin-Aizenberg
authors: Fereidoun
authors: F
authors: Abtin
authors: Hyun J
authors: HJ
authors: Kim
authors: Peiyun
authors: P
authors: Lu
authors: Bram
authors: B
authors: van Ginneken
authors: Greg
authors: G
authors: Shaw
authors: Matthew S
authors: MS
authors: Brown
mesh_terms: D000465:Algorithms; D001331:Automation; D015992:Body Mass Index; D001330:Electronic Data Processing; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D008168:Lung / Q000000981:diagnostic imaging*; D008297:Male; D015588:Observer Variation; D010363:Pattern Recognition, Automated; D029424:Pulmonary Disease, Chronic Obstructive / Q000175:diagnosis* / Q000000981:diagnostic imaging*; D011656:Pulmonary Emphysema / Q000175:diagnosis* / Q000000981:diagnostic imaging*; D012372:ROC Curve; D012984:Software; D014057:Tomography, X-Ray Computed / Q000379:methods*