title: Pulmonary lobar volumetry using novel volumetric computer-aided diagnosis and computed tomography.
abstract: OBJECTIVE
  To compare the accuracy of pulmonary lobar volumetry using the conventional number of segments method and novel volumetric computer-aided diagnosis using 3D computed tomography images.
  
  
  METHODS
  We acquired 50 consecutive preoperative 3D computed tomography examinations for lung tumours reconstructed at 1-mm slice thicknesses. We calculated the lobar volume and the emphysematous lobar volume < -950 HU of each lobe using (i) the slice-by-slice method (reference standard), (ii) number of segments method, and (iii) semi-automatic and (iv) automatic computer-aided diagnosis. We determined Pearson correlation coefficients between the reference standard and the three other methods for lobar volumes and emphysematous lobar volumes. We also compared the relative errors among the three measurement methods.
  
  
  RESULTS
  Both semi-automatic and automatic computer-aided diagnosis results were more strongly correlated with the reference standard than the number of segments method. The correlation coefficients for automatic computer-aided diagnosis were slightly lower than those for semi-automatic computer-aided diagnosis because there was one outlier among 50 cases (2%) in the right upper lobe and two outliers among 50 cases (4%) in the other lobes. The number of segments method relative error was significantly greater than those for semi-automatic and automatic computer-aided diagnosis (P < 0.001). The computational time for automatic computer-aided diagnosis was 1/2 to 2/3 than that of semi-automatic computer-aided diagnosis.
  
  
  CONCLUSIONS
  A novel lobar volumetry computer-aided diagnosis system could more precisely measure lobar volumes than the conventional number of segments method. Because semi-automatic computer-aided diagnosis and automatic computer-aided diagnosis were complementary, in clinical use, it would be more practical to first measure volumes by automatic computer-aided diagnosis, and then use semi-automatic measurements if automatic computer-aided diagnosis failed.
authors.affiliation: Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Japan. iwano45@med.nagoya-u.ac.jp
authors.forename: Shingo
authors.initials: S
authors.lastname: Iwano
authors.forename: Mariko
authors.initials: M
authors.lastname: Kitano
authors.forename: Keiji
authors.initials: K
authors.lastname: Matsuo
authors.forename: Kenichi
authors.initials: K
authors.lastname: Kawakami
authors.forename: Wataru
authors.initials: W
authors.lastname: Koike
authors.forename: Mariko
authors.initials: M
authors.lastname: Kishimoto
authors.forename: Tsutomu
authors.initials: T
authors.lastname: Inoue
authors.forename: Yuanzhong
authors.initials: Y
authors.lastname: Li
authors.forename: Shinji
authors.initials: S
authors.lastname: Naganawa
mesh_terms: D000328:Adult; D000368:Aged; D057205:Automation, Laboratory; D005260:Female; D006801:Humans; D021621:Imaging, Three-Dimensional; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D008176:Lung Volume Measurements / Q000379:methods*; D008297:Male; D008875:Middle Aged; D061330:Multidetector Computed Tomography; D011237:Predictive Value of Tests; D011857:Radiographic Image Interpretation, Computer-Assisted
keywords: Computed tomography; Computer applications; Lobectomy; Lung cancer surgery; Pulmonary function