title: Semiautomated segmentation of pleural effusions in MDCT datasets.
abstract: OBJECTIVE
  To develop and evaluate a novel algorithm for semiautomated segmentation and volumetry of pleural effusions in multidetector computed tomography (MDCT) datasets.
  
  
  METHODS
  A seven-step algorithm for semiautomated segmentation of pleural effusions in MDCT datasets was developed, mainly using algorithms from the ITK image processing library. Semiautomated segmentation of pleural effusions was performed in 40 MDCT datasets of the chest (males = 22, females = 18, mean age: 56.7 +/- 19.3 years). The accuracy of the semiautomated segmentation as compared with a manual segmentation approach was quantified based on the differences of the segmented volumes, the degree of over-/undersegmentation, and the Hausdorff distance. The time needed for the semiautomated and the manual segmentation process were recorded and compared.
  
  
  RESULTS
  The mean volume of the pleural effusions was 557.30 mL (+/- 477.27 mL) for the semiautomated and 553.19 (+/- 473.49 mL) for the manual segmentation. The difference was not statistically significant (Student t-test, P = .133). Regression analysis confirmed a strong relationship between the semiautomated algorithm and the gold standard (r(2) = 0.998). Mean overlap of the segmented areas was 79% (+/- 9.3%) over all datasets with moderate oversegmentation (22% +/- 9.3%) and undersegmentation (21% +/- 9.7%). The mean Hausdorff distance was 17.2 mm (+/- 8.35 mm). The mean duration of the semiautomated segmentation process with user interaction was 8.4 minutes (+/- 2.6 minutes) as compared to 32.9 minutes (+/- 17.4 minutes) for manual segmentation.
  
  
  CONCLUSIONS
  The semiautomated algorithm for segmentation and volumetry of pleural effusions in MDCT datasets shows a high diagnostic accuracy when compared with manual segmentation.
authors.affiliation: Hannover Medical School, Institute of Radiology, Hannover, Germany. c.v.falck@gmx.de <c.v.falck@gmx.de>
authors.forename: Christian
authors.initials: C
authors.lastname: von Falck
authors.forename: Simone
authors.initials: S
authors.lastname: Meier
authors.forename: Steffen
authors.initials: S
authors.lastname: Jördens
authors.forename: Benjamin
authors.initials: B
authors.lastname: King
authors.forename: Michael
authors.initials: M
authors.lastname: Galanski
authors.forename: Hoen-oh
authors.initials: HO
authors.lastname: Shin
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D010996:Pleural Effusion / Q000000981:diagnostic imaging*; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*