title: 3D shape analysis of the brain's third ventricle using a midplane encoded symmetric template model.
abstract: BACKGROUND
  Structural changes of the brain's third ventricle have been acknowledged as an indicative measure of the brain atrophy progression in neurodegenerative and endocrinal diseases. To investigate the ventricular enlargement in relation to the atrophy of the surrounding structures, shape analysis is a promising approach. However, there are hurdles in modeling the third ventricle shape. First, it has topological variations across individuals due to the inter-thalamic adhesion. In addition, as an interhemispheric structure, it needs to be aligned to the midsagittal plane to assess its asymmetric and regional deformation.
  
  
  METHODS
  To address these issues, we propose a model-based shape assessment. Our template model of the third ventricle consists of a midplane and a symmetric mesh of generic shape. By mapping the template's midplane to the individuals' brain midsagittal plane, we align the symmetric mesh on the midline of the brain before quantifying the third ventricle shape. To build the vertex-wise correspondence between the individual third ventricle and the template mesh, we employ a minimal-distortion surface deformation framework. In addition, to account for topological variations, we implement geometric constraints guiding the template mesh to have zero width where the inter-thalamic adhesion passes through, preventing vertices crossing between left and right walls of the third ventricle. The individual shapes are compared using a vertex-wise deformity from the symmetric template.
  
  
  RESULTS
  Experiments on imaging and demographic data from a study of aging showed that our model was sensitive in assessing morphological differences between individuals in relation to brain volume (i.e. proxy for general brain atrophy), gender and the fluid intelligence at age 72. It also revealed that the proposed method can detect the regional and asymmetrical deformation unlike the conventional measures: volume (median 1.95ml, IQR 0.96ml) and width of the third ventricle. Similarity measures between binary masks and the shape model showed that the latter reconstructed shape details with high accuracy (Dice coefficient ≥0.9, mean distance 0.5mm and Hausdorff distance 2.7mm).
  
  
  CONCLUSIONS
  We have demonstrated that our approach is suitable to morphometrical analyses of the third ventricle, providing high accuracy and inter-subject consistency in the shape quantification. This shape modeling method with geometric constraints based on anatomical landmarks could be extended to other brain structures which require a consistent measurement basis in the morphometry.
authors.affiliation: School of Computing, Korea Advanced Institute of Science and Technology, Daejeon, South Korea.
authors.forename: Jaeil
authors.initials: J
authors.lastname: Kim
authors.affiliation: Brain Research Imaging Centre, Department of Neuroimaging Sciences, University of Edinburgh, Edinburgh, UK; Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK; SINAPSE (Scottish Imaging Network, A Platform for Scientific Excellence) Collaboration, Scotland, UK.
authors.forename: Maria del C
authors.initials: Mdel C
authors.lastname: Valdés Hernández
authors.affiliation: Brain Research Imaging Centre, Department of Neuroimaging Sciences, University of Edinburgh, Edinburgh, UK; Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK; SINAPSE (Scottish Imaging Network, A Platform for Scientific Excellence) Collaboration, Scotland, UK.
authors.forename: Natalie A
authors.initials: NA
authors.lastname: Royle
authors.affiliation: Brain Research Imaging Centre, Department of Neuroimaging Sciences, University of Edinburgh, Edinburgh, UK; Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK; SINAPSE (Scottish Imaging Network, A Platform for Scientific Excellence) Collaboration, Scotland, UK.
authors.forename: Susana Muñoz
authors.initials: SM
authors.lastname: Maniega
authors.affiliation: Brain Research Imaging Centre, Department of Neuroimaging Sciences, University of Edinburgh, Edinburgh, UK; Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK; SINAPSE (Scottish Imaging Network, A Platform for Scientific Excellence) Collaboration, Scotland, UK; Computer Science Department, Lagos State University, Nigeria.
authors.forename: Benjamin S
authors.initials: BS
authors.lastname: Aribisala
authors.affiliation: Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK; Psychology, School of Life Sciences, Heriot-Watt University, Edinburgh, UK.
authors.forename: Alan J
authors.initials: AJ
authors.lastname: Gow
authors.affiliation: Brain Research Imaging Centre, Department of Neuroimaging Sciences, University of Edinburgh, Edinburgh, UK; Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK; SINAPSE (Scottish Imaging Network, A Platform for Scientific Excellence) Collaboration, Scotland, UK.
authors.forename: Mark E
authors.initials: ME
authors.lastname: Bastin
authors.affiliation: Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK; SINAPSE (Scottish Imaging Network, A Platform for Scientific Excellence) Collaboration, Scotland, UK; Department of Psychology, University of Edinburgh, Edinburgh, UK.
authors.forename: Ian J
authors.initials: IJ
authors.lastname: Deary
authors.affiliation: Brain Research Imaging Centre, Department of Neuroimaging Sciences, University of Edinburgh, Edinburgh, UK; Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK; SINAPSE (Scottish Imaging Network, A Platform for Scientific Excellence) Collaboration, Scotland, UK.
authors.forename: Joanna M
authors.initials: JM
authors.lastname: Wardlaw
authors.affiliation: School of Computing, Korea Advanced Institute of Science and Technology, Daejeon, South Korea. Electronic address: jinahpark@kaist.ac.kr.
authors.forename: Jinah
authors.initials: J
authors.lastname: Park
mesh_terms: D000375:Aging; D003071:Cognition; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods; D008953:Models, Anatomic; D020542:Third Ventricle / Q000033:anatomy & histology*
keywords: 3D model; Aging; Atrophy; Brain; Shape analysis; Third ventricle