title: Automatic model-guided segmentation of the human brain ventricular system from CT images.
abstract: OBJECTIVE
  Accurate segmentation of the brain ventricular system on computed tomographic (CT) imaging is useful in neurodiagnosis and neurosurgery. Manual segmentation is time consuming, usually not reproducible, and subjective. Because of image noise, low contrast between soft tissues, large interslice distance, large shape, and size variations of the ventricular system, no automatic method is presently available. The authors propose a model-guided method for the automated segmentation of the ventricular system.
  
  
  METHODS
  Fifty CT scans of patients with strokes at different sites were collected for this study. Given a brain CT image, its ventricular system was segmented in five steps: (1) a predefined volumetric model was registered (or deformed) onto the image; (2) according to the deformed model, eight regions of interest were automatically specified; (3) the intensity threshold of cerebrospinal fluid was calculated in a region of interest and used to segment all regions of cerebrospinal fluid from the entire brain volume; (4) each ventricle was segmented in its specified region of interest; and (5) intraventricular calcification regions were identified to refine the ventricular segmentation.
  
  
  RESULTS
  Compared to ground truths provided by experts, the segmentation results of this method achieved an average overlap ratio of 85% for the entire ventricular system. On a desktop personal computer with a dual-core central processing unit running at 2.13 GHz, about 10 seconds were required to analyze each data set.
  
  
  CONCLUSIONS
  Experiments with clinical CT images showed that the proposed method can generate acceptable results in the presence of image noise, large shape, and size variations of the ventricular system, and therefore it is potentially useful for the quantitative interpretation of CT images in neurodiagnosis and neurosurgery.
authors.affiliation: Biomedical Imaging Lab, Singapore Bioimaging Consortium, 30 Bioplolis Street #07-01, Matrix, Singapore. liujm@sbic.a-star.edu.sg
authors.forename: Jimin
authors.initials: J
authors.lastname: Liu
authors.forename: Su
authors.initials: S
authors.lastname: Huang
authors.forename: Volkau
authors.initials: V
authors.lastname: Ihar
authors.forename: Wojciech
authors.initials: W
authors.lastname: Ambrosius
authors.forename: Looi Chow
authors.initials: LC
authors.lastname: Lee
authors.forename: Wieslaw L
authors.initials: WL
authors.lastname: Nowinski
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D002552:Cerebral Ventricles; D014695:Cerebral Ventriculography / Q000379:methods*; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; 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*