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Brain Morphometry

The Experts below are selected from a list of 2265 Experts worldwide ranked by ideXlab platform

Xianfeng Gu – 1st expert on this subject based on the ideXlab platform

  • MBIA/MFCA@MICCAI – Surface Foliation Based Brain Morphometry Analysis
    Multimodal Brain Image Analysis and Mathematical Foundations of Computational Anatomy, 2019
    Co-Authors: Ming Ma, Yalin Wang, Xin Qi, Wen Zhang, Xianfeng Gu

    Abstract:

    Brain Morphometry plays a fundamental role in neuroimaging research. In this work, we propose a novel method for Brain surface Morphometry analysis based on surface foliation theory. Given Brain cortical surfaces with automatically extracted landmark curves, we first construct finite foliations on surfaces. A set of admissible curves and a height parameter for each loop are provided by users. The admissible curves cut the surface into a set of pairs of pants. A pants decomposition graph is then constructed. Strebel differential is obtained by computing a unique harmonic map from surface to pants decomposition graph. The critical trajectories of Strebel differential decompose the surface into topological cylinders. After conformally mapping those topological cylinders to standard cylinders, parameters of standard cylinders (height, circumference) are intrinsic geometric features of the original cortical surfaces and thus can be used for Morphometry analysis purpose. In this work, we propose a set of novel surface features. To the best of our knowledge, this is the first work to make use of surface foliation theory for Brain Morphometry analysis. The features we computed are intrinsic and informative. The proposed method is rigorous, geometric, and automatic. Experimental results on classifying Brain cortical surfaces between patients with Alzheimer’s disease and healthy control subjects demonstrate the efficiency and efficacy of our method.

  • Optimal mass transport based Brain Morphometry for patients with congenital hand deformities
    The Visual Computer, 2018
    Co-Authors: Ming Ma, Xu Wang, Ye Duan, Scott H. Frey, Xianfeng Gu

    Abstract:

    Congenital hand deformities (CHD) have attracted increasing research attention in the past few decades. The impacts of CHD on the Brain structure, however, are not fully studied to date. In this work, we propose a novel framework to study Brain Morphometry in CHD patients using Wasserstein distance based on optimal mass transport (OMT) theory. We first employ conformal mapping to map the left and right surface-based functional Brain areas to planar rectangles, which pushes the area element on the Brain surface to the planar rectangle and incurs the area distortion. A measure is then determined by this area distortion. We further propose a new rectangle domain-based OMT map algorithm. Given two measures on two surfaces, we employ the proposed algorithm to compute a unique OMT map between the two measures encoding the geometric information of left and right surface-based functional Brain areas. The transportation cost of this OMT map gives the Wasserstein distance between two surfaces, which intrinsically measures the dissimilarities between two surface-based shapes. Our method is theoretically rigorous and computationally efficient and stable. We finally evaluate the proposed Wasserstein distance-based method on the left and right post-central gyri from the CHD patients and healthy control subjects for analyzing Brain cortical Morphometry. Experimental results demonstrate the efficiency and efficacy of our method, and shed insightful lights on the study of the Brain Morphometry for those subjects with CHD.

  • IPMI – Shape Classification Using Wasserstein Distance for Brain Morphometry Analysis.
    Information processing in medical imaging : proceedings of the … conference, 2015
    Co-Authors: Zhengyu Su, Wei Zeng, Yalin Wang, Zhong-lin Lu, Xianfeng Gu

    Abstract:

    Brain Morphometry study plays a fundamental role in medical imaging analysis and diagnosis. This work proposes a novel framework for Brain cortical surface classification using Wasserstein distance, based on uniformization theory and Riemannian optimal mass transport theory.

Yalin Wang – 2nd expert on this subject based on the ideXlab platform

  • MBIA/MFCA@MICCAI – Surface Foliation Based Brain Morphometry Analysis
    Multimodal Brain Image Analysis and Mathematical Foundations of Computational Anatomy, 2019
    Co-Authors: Ming Ma, Yalin Wang, Xin Qi, Wen Zhang, Xianfeng Gu

    Abstract:

    Brain Morphometry plays a fundamental role in neuroimaging research. In this work, we propose a novel method for Brain surface Morphometry analysis based on surface foliation theory. Given Brain cortical surfaces with automatically extracted landmark curves, we first construct finite foliations on surfaces. A set of admissible curves and a height parameter for each loop are provided by users. The admissible curves cut the surface into a set of pairs of pants. A pants decomposition graph is then constructed. Strebel differential is obtained by computing a unique harmonic map from surface to pants decomposition graph. The critical trajectories of Strebel differential decompose the surface into topological cylinders. After conformally mapping those topological cylinders to standard cylinders, parameters of standard cylinders (height, circumference) are intrinsic geometric features of the original cortical surfaces and thus can be used for Morphometry analysis purpose. In this work, we propose a set of novel surface features. To the best of our knowledge, this is the first work to make use of surface foliation theory for Brain Morphometry analysis. The features we computed are intrinsic and informative. The proposed method is rigorous, geometric, and automatic. Experimental results on classifying Brain cortical surfaces between patients with Alzheimer’s disease and healthy control subjects demonstrate the efficiency and efficacy of our method.

  • Brain Morphometry Analysis with Surface Foliation Theory
    arXiv: Computational Geometry, 2018
    Co-Authors: Ming Ma, Yalin Wang, Xin Qi, Wen Zhang, David Xianfeng Gu

    Abstract:

    Brain Morphometry study plays a fundamental role in neuroimaging research. In this work, we propose a novel method for Brain surface Morphometry analysis based on surface foliation theory. Given Brain cortical surfaces with automatically extracted landmark curves, we first construct finite foliations on surfaces. A set of admissible curves and a height parameter for each loop are provided by users. The admissible curves cut the surface into a set of pairs of pants. A pants decomposition graph is then constructed. Strebel differential is obtained by computing a unique harmonic map from surface to pants decomposition graph. The critical trajectories of Strebel differential decompose the surface into topological cylinders. After conformally mapping those topological cylinders to standard cylinders, parameters of standard cylinders (height, circumference) are intrinsic geometric features of the original cortical surfaces and thus can be used for Morphometry analysis purpose. In this work, we propose a set of novel surface features rooted in surface foliation theory. To the best of our knowledge, this is the first work to make use of surface foliation theory for Brain Morphometry analysis. The features we computed are intrinsic and informative. The proposed method is rigorous, geometric, and automatic. Experimental results on classifying Brain cortical surfaces between patients with Alzheimer’s disease and healthy control subjects demonstrate the efficiency and efficacy of our method.

  • Conformal invariants for multiply connected surfaces: Application to landmark curve-based Brain Morphometry analysis
    Medical Image Analysis, 2016
    Co-Authors: Wen Zhang, Miao Tang, Richard J. Caselli, Yalin Wang

    Abstract:

    Abstract Landmark curves were widely adopted in neuroimaging research for surface correspondence computation and quantified Morphometry analysis. However, most of the landmark based Morphometry studies only focused on landmark curve shape difference. Here we propose to compute a set of conformal invariant-based shape indices, which are associated with the landmark curve induced boundary lengths in the hyperbolic parameter domain. Such shape indices may be used to identify which surfaces are conformally equivalent and further quantitatively measure surface deformation. With the surface Ricci flow method, we can conformally map a multiply connected surface to the Poincare disk. Our algorithm provides a stable method to compute the shape index values in the 2D (Poincare Disk) parameter domain. The proposed shape indices are succinct, intrinsic and informative. Experimental results with synthetic data and 3D MRI data demonstrate that our method is invariant under isometric transformations and able to detect Brain surface abnormalities. We also applied the new shape indices to analyze Brain Morphometry abnormalities associated with Alzheimer’ s disease (AD). We studied the baseline MRI scans of a set of healthy control and AD patients from the Alzheimer’ s Disease Neuroimaging Initiative (ADNI: 30 healthy control subjects vs. 30 AD patients). Although the lengths of the landmarks in Euclidean space, cortical surface area, and volume features did not differ between the two groups, our conformal invariant based shape indices revealed significant differences by Hotelling’ s T2 test. The novel conformal invariant shape indices may offer a new sensitive biomarker and enrich our Brain imaging analysis toolset for studying diagnosis and prognosis of AD.

Paul M Thompson – 3rd expert on this subject based on the ideXlab platform

  • New approaches in Brain Morphometry.
    American Journal of Geriatric Psychiatry, 2013
    Co-Authors: Arthur W Toga, Paul M Thompson

    Abstract:

    The complexity and variability of the human Brain across subjects is so great that reliance on maps and atlases is essential to effectively manipulate, analyze, and interpret Brain data. Central to these tasks is the construction of averages, templates, and models to describe how the Brain and its component parts are organized. Design of appropriate reference systems for human Brain data presents considerable challenges because these systems must capture how Brain structure and function vary in large populations, across age and gender, in different disease states, across imaging modalities, and even across species. The authors introduce the topic of Brain maps as applied to a variety of questions and problems in health and disease and include a brief survey of the types of maps relevant to mental disorders, including maps that capture dynamic patterns of Brain change in dementia.

  • teichmuller shape space theory and its application to Brain Morphometry
    Medical Image Computing and Computer-Assisted Intervention, 2009
    Co-Authors: Yalin Wang, Xianfeng Gu, Tony F Chan, Arthur W Toga, Paul M Thompson

    Abstract:

    Here we propose a novel method to compute Teichmuller shape space based shape index to study Brain Morphometry. Such a shape index is intrinsic, and invariant under conformal transformations, rigid motions and scaling. We conformally map a genus-zero open boundary surface to the Poincare disk with the Yamabe flow method. The shape indices that we compute are the lengths of a special set of geodesics under hyperbolic metric. Tests on longitudinal Brain imaging data were used to demonstrate the stability of the derived feature vectors. In leave-one-out validation tests, we achieved 100% accurate classification (versus only 68% accuracy for volume measures) in distinguishing 11 HIV/AIDS individuals from 8 healthy control subjects, based on Teichmuller coordinates for lateral ventricular surfaces extracted from their 3D MRI scans.

  • MICCAI (1) – Teichmüller Shape Space Theory and Its Application to Brain Morphometry
    Medical image computing and computer-assisted intervention : MICCAI … International Conference on Medical Image Computing and Computer-Assisted Inte, 2009
    Co-Authors: Yalin Wang, Xianfeng Gu, Tony F Chan, Arthur W Toga, Paul M Thompson

    Abstract:

    Here we propose a novel method to compute Teichmuller shape space based shape index to study Brain Morphometry. Such a shape index is intrinsic, and invariant under conformal transformations, rigid motions and scaling. We conformally map a genus-zero open boundary surface to the Poincare disk with the Yamabe flow method. The shape indices that we compute are the lengths of a special set of geodesics under hyperbolic metric. Tests on longitudinal Brain imaging data were used to demonstrate the stability of the derived feature vectors. In leave-one-out validation tests, we achieved 100% accurate classification (versus only 68% accuracy for volume measures) in distinguishing 11 HIV/AIDS individuals from 8 healthy control subjects, based on Teichmuller coordinates for lateral ventricular surfaces extracted from their 3D MRI scans.