title: Automated CT-based segmentation and quantification of total intracranial volume.
abstract: OBJECTIVE
  To develop an algorithm to segment and obtain an estimate of total intracranial volume (tICV) from computed tomography (CT) images.
  
  
  METHODS
  Thirty-six CT examinations from 18 patients were included. Ten patients were examined twice the same day and eight patients twice six months apart (these patients also underwent MRI). The algorithm combines morphological operations, intensity thresholding and mixture modelling. The method was validated against manual delineation and its robustness assessed from repeated imaging examinations. Using automated MRI software, the comparability with MRI was investigated. Volumes were compared based on average relative volume differences and their magnitudes; agreement was shown by a Bland-Altman analysis graph.
  
  
  RESULTS
  We observed good agreement between our algorithm and manual delineation of a trained radiologist: the Pearson's correlation coefficient was r = 0.94, tICVml[manual] = 1.05 × tICVml[automated] - 33.78 (R(2) = 0.88). Bland-Altman analysis showed a bias of 31 mL and a standard deviation of 30 mL over a range of 1265 to 1526 mL.
  
  
  CONCLUSIONS
  tICV measurements derived from CT using our proposed algorithm have shown to be reliable and consistent compared to manual delineation. However, it appears difficult to directly compare tICV measures between CT and MRI.
  
  
  CONCLUSIONS
  • Automated estimation of tICV is in good agreement with manual tracing. • Consistent tICV estimations from repeated measurements demonstrate the robustness of the algorithm. • Automatically segmented volumes seem less variable than those from manual tracing. • Unbiased and automated tlCV estimation is possible from CT.
authors: Department of Neurobiology, Care Sciences and Society (NVS), Division of Clinical Geriatrics, Karolinska Institute, Novum, Blickagången 6, 14157, Stockholm, Sweden. carlos.aguilar@ki.se.
authors: Carlos
authors: C
authors: Aguilar
authors: Department of Clinical Science, Intervention and Technology, Division of Medical Imaging and Technology, Karolinska Institute, Stockholm, Sweden.|Department of Radiology, Karolinska University Hospital in Huddinge, Stockholm, Sweden.
authors: Kaijsa
authors: K
authors: Edholm
authors: Institute of Psychiatry, King's College London, London, UK.|NIHR Biomedical Research Centre for Mental Health and Biomedical Research Unit for Dementia, London, UK.
authors: Andrew
authors: A
authors: Simmons
authors: Department of Clinical Science, Intervention and Technology, Division of Medical Imaging and Technology, Karolinska Institute, Stockholm, Sweden.|Department of Radiology, Karolinska University Hospital in Huddinge, Stockholm, Sweden.
authors: Lena
authors: L
authors: Cavallin
authors: Department of Clinical Science, Intervention and Technology, Division of Medical Imaging and Technology, Karolinska Institute, Stockholm, Sweden.|Department of Radiology, Karolinska University Hospital in Huddinge, Stockholm, Sweden.
authors: Susanne
authors: S
authors: Muller
authors: Department of Psychiatry and Neurochemistry, The Sahlgrenska Academy, Gothenburg University, Gothenburg, Sweden.
authors: Ingmar
authors: I
authors: Skoog
authors: Department of Surgical Sciences, Radiology, Akademiska Sjukhuset, Uppsala University, Uppsala, Sweden.
authors: Elna-Marie
authors: EM
authors: Larsson
authors: Department of Clinical Science, Intervention and Technology, Division of Medical Imaging and Technology, Karolinska Institute, Stockholm, Sweden.|Department of Radiology, Karolinska University Hospital in Huddinge, Stockholm, Sweden.
authors: Rimma
authors: R
authors: Axelsson
authors: Department of Neurobiology, Care Sciences and Society (NVS), Division of Clinical Geriatrics, Karolinska Institute, Novum, Blickagången 6, 14157, Stockholm, Sweden.
authors: Lars-Olof
authors: LO
authors: Wahlund
authors: Department of Neurobiology, Care Sciences and Society (NVS), Division of Clinical Geriatrics, Karolinska Institute, Novum, Blickagången 6, 14157, Stockholm, Sweden.
authors: Eric
authors: E
authors: Westman
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001921:Brain / Q000000981:diagnostic imaging*; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods* / Q000706:statistics & numerical data; D016013:Likelihood Functions; D008279:Magnetic Resonance Imaging / Q000379:methods / Q000706:statistics & numerical data; D008297:Male; D008875:Middle Aged; D009929:Organ Size; D019047:Phantoms, Imaging; D015203:Reproducibility of Results; D012189:Retrospective Studies; D014057:Tomography, X-Ray Computed / Q000379:methods* / Q000706:statistics & numerical data
keywords: Computed tomography; Magnetic resonance imaging; Maximum likelihood estimator; Skull stripping; Total intracranial volume