Evidence map›Paper›PMID 27084320›Full record

ArticleComputer methods and programs in biomedicine2016

3D shape analysis of the brain's third ventricle using a midplane encoded symmetric template model.

Jaeil Kim, Maria del C Valdés Hernández, Natalie A Royle, Susana Muñoz Maniega, Benjamin S Aribisala, Alan J Gow, Mark E Bastin, Ian J Deary, Joanna M Wardlaw, Jinah Park

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Article in Computer methods and programs in biomedicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in
1citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Jaeil KimSchool of Computing, Korea Advanced Institute of Science and Technology, Daejeon, South Korea.
Maria del C Valdés HernándezBrain 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.
Natalie A RoyleBrain 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.
Susana Muñoz ManiegaBrain 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.
Benjamin S AribisalaBrain 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.
Alan J GowCentre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK; Psychology, School of Life Sciences, Heriot-Watt University, Edinburgh, UK.
Mark E BastinBrain 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.
Ian J DearyCentre 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.
Joanna M WardlawBrain 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.
Jinah ParkSchool of Computing, Korea Advanced Institute of Science and Technology, Daejeon, South Korea. Electronic address: jinahpark@kaist.ac.kr.

Funding

Medical Research Council G0701120Medical Research Council G1001245Medical Research Council MR/K026992/1Medical Research Council MR/M013111/1Wellcome Trust
6 · The paper itself

Abstract

backgroundStructural 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.

methodTo 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.

resultsExperiments 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).

conclusionsWe 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.

Indexed as

Models, AnatomicAgingCognitionHumansMagnetic Resonance ImagingThird Ventricle3D modelAgingAtrophyBrainShape analysisThird ventricle

Identifiers

PMID27084320
PMCPMC4841787

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