Statistical Analysis of Anatomy from Medical Images Tom Fletcher School of Computing University of Utah National Alliance for Medical Image Computing.

Slides:

Advertisements

Similar presentations

Sarang Joshi #1 Computational Anatomy: Simple Statistics on Interesting Spaces Sarang Joshi, Tom Fletcher Scientific Computing and Imaging Institute Department.

Advertisements

NA-MIC National Alliance for Medical Image Computing Longitudinal and Time- Series Analysis Everyone in NA-MIC Core 1 and 2.

National Alliance for Medical Image Computing Slide 1 NAMIC at UNC DTI, Shape and Longitudinal registration Closely linked with Utah.

National Alliance for Medical Image Computing Diffusion Weighted MRI.

NA-MIC National Alliance for Medical Image Computing Skull Stripper Extension Jim Miller GE Research.

Camino and DTI-TK: Advanced Diffusion MRI Pipeline for Traumatic Brain Injury Gary Hui Zhang, PhD Microstructure Imaging Group Centre for Medical Image.

Quality Control of Diffusion Weighted Images

NA-MIC National Alliance for Medical Image Computing DTI Atlas Registration via 3D Slicer and DTI-Reg Martin Styner, UNC Guido Gerig,

NAMIC: UNC – PNL collaboration- 1 - October 7, 2005 Fiber tract-oriented quantitative analysis of Diffusion Tensor MRI data Postdoctoral fellow, Dept of.

The Microstructural Basis of Abnormal Connectivity in Autism Janet Lainhart, MD Associate Professor of Psychiatry, Pediatrics, Psychology, and Faculty.

Sponsor: Prof. Sidney Spector Computational anatomy to assess growth pattern of early brain development in healthy and disease populations Guido Gerig.

Preliminary Results Longitudinal Change Conclusions Acknowledgements The most striking result of the longitudinal growth analysis between 2 and 4 years.

Diffusion Tensor Imaging (DTI) is becoming a routine technique to study white matter properties and alterations of fiber integrity due to pathology. The.

NAMIC: UNC – PNL collaboration- 1 - October 7, 2005 Quantitative Analysis of Diffusion Tensor Measurements along White Matter Tracts Postdoctoral fellow,

The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL FADTTS: Functional Analysis of Diffusion Tensor Tract Statistics Hongtu Zhu, Ph.D. Department of Biostatistics.

NA-MIC National Alliance for Medical Image Computing DTI atlas building for population analysis: Application to PNL SZ study Casey Goodlett,

National Alliance for Medical Image Computing – Algorithms Core (C1a) Five investigators: –A. Tannenbaum (BU), P. Golland (MIT), M. Styner.

Automatic Brain Segmentation in Rhesus Monkeys February 2006, SPIE Medical Imaging 2006 Funding provided by UNC Neurodevelopmental Disorders Research Center.

NA-MIC National Alliance for Medical Image Computing Core 1 & Core 3 Projects.

NA-MIC National Alliance for Medical Image Computing Shape Analysis and Cortical Correspondence Martin Styner Core 1 (Algorithms), UNC.

National Alliance for Medical Image Computing UNC: Quantitative DTI Analysis Guido Gerig, Isabelle Corouge Students: Casey Goodlett,

NA-MIC National Alliance for Medical Image Computing DTI Atlas Registration via 3D Slicer and DTI-Reg Martin Styner, UNC Clement Vachet,

Algorithms Organization Ross Whitaker – Utah Polina Golland – MIT (Kayhan B.) Guido Gerig – Utah Martin Styner – UNC Allen Tannenbaum – Georgia Tech.

NA-MIC National Alliance for Medical Image Computing ABC: Atlas-Based Classification Marcel Prastawa and Guido Gerig Scientific Computing.

NA-MIC National Alliance for Medical Image Computing Validation of DTI Analysis Guido Gerig, Clement Vachet, Isabelle Corouge, Casey.

NA-MIC National Alliance for Medical Image Computing NAMIC UNC Site Update Site PI: Martin Styner Site NAMIC folks: Clement Vachet, Gwendoline.

DTI Quality Control Assessment via Error Estimation From Monte Carlo Simulations February 2013, SPIE Medical Imaging 2013 MC Simulation for Error-based.

NCBC EAB, January 2010 NA-MIC Highlights: A Core 1 Perspective Ross Whitaker University of Utah National Alliance for Biomedical Image Computing.

NA-MIC National Alliance for Medical Image Computing A longitudinal study of brain development in autism Heather Cody Hazlett, PhD Neurodevelopmental.

NA-MIC National Alliance for Medical Image Computing National Alliance for Medical Image Computing: NAMIC Ron Kikinis, M.D.

NA-MIC National Alliance for Medical Image Computing Core 3.1 Activities Harvard - Psychiatry Neuroimaging Laboratory and Dartmouth -

NA-MIC National Alliance for Medical Image Computing Process-, Work-Flow in Medical Image Processing Guido Gerig

NA-MIC National Alliance for Medical Image Computing UNC Core 1: What did we do for NA-MIC and/or what did NA-MIC do for us Guido Gerig,

NA-MIC National Alliance for Medical Image Computing NA-MIC UNC Guido Gerig, Martin Styner, Isabelle Corouge

NA-MIC National Alliance for Medical Image Computing NAMIC UNC Site Update Site PI: Martin Styner UNC Site NAMIC folks: C Vachet, G Roger,

Subjects are registered to a template using affine transformations. These affine transformations are used to align the tracts passing through the splenium.

NA-MIC National Alliance for Medical Image Computing A longitudinal study of brain development in autism Heather Cody Hazlett, PhD Neurodevelopmental.

NA-MIC National Alliance for Medical Image Computing A longitudinal study of brain development in autism Heather Cody Hazlett, PhD Neurodevelopmental.

Statistical Shape Analysis of Multi-object Complexes June 2007, CVPR 2007 Funding provided by NIH NIBIB grant P01EB and NIH Conte Center MH

Analysis of Longitudinal Shape Variability via Subject Specific Growth Modeling James Fishbaugh 1 Marcel Prastawa 1 Stanley Durrleman 2 Joseph Piven 3.

NA-MIC National Alliance for Medical Image Computing Velocardiofacial Syndrome as a Genetic Model for Schizophrenia Marek Kubicki DBP2,

Corpus Callosum Probabilistic Subdivision based on Inter-Hemispheric Connectivity May 2005, UNC Radiology Symposium Original brain images for the corpus.

NA-MIC National Alliance for Medical Image Computing UNC/Utah-II Core 1 Guido Gerig, Casey Goodlett, Marcel Prastawa, Sylvain Gouttard.

NA-MIC National Alliance for Medical Image Computing Velocardiofacial Syndrome as a Genetic Model for Schizophrenia Marek Kubicki DBP2,

Department of Psychiatry, Department of Computer Science, 3 Carolina Institute for Developmental Disabilities 1 Department of Psychiatry, 2 Department.

NA-MIC National Alliance for Medical Image Computing Core 5: Training.

Geodesic image regression with a sparse parameterization of diffeomorphisms James Fishbaugh 1 Marcel Prastawa 1 Guido Gerig 1 Stanley Durrleman 2 1 Scientific.

NA-MIC National Alliance for Medical Image Computing NAMIC Core 3.1 Overview: Harvard/BWH and Dartmouth Structural and Functional Connectivity.

Segmentation of Single-Figure Objects by Deformable M-reps

Corpus Callosum Probabilistic Subdivision based on Inter-Hemispheric Connectivity Martin Styner1,2, Ipek Oguz1, Rachel Gimpel Smith2, Carissa Cascio2,

Diffusion tensor imaging and Tract-Based Spatial Statistics in Alzheimer's disease and mild cognitive impairment Yawu Liu, Gabriela Spulber, Kimmo K.

Shape Analysis: Description &Framework Develop a generally applicable description for statistical shape analysis studies, as well as a computational framework.

RESULTS AND DISCUSSION

Diffusion Image Analysis

Riemannian DTI Filters: Develop algorithms and ITK modules for basic image processing on tensor fields using Riemannian approaches. Team Plan/Expected.

Healthy Aging by Staying Selectively Connected: A Mini-Review

Figure 1 . Brain MRI (T1 axial image post gadolinium) showing mass-like heterogeneous enhancement involving the right caudate nucleus.

Linear Regression.

Keith Worsley Keith Worsley

Tobias Heimann - DKFZ Ipek Oguz - UNC Ivo Wolf - DKFZ

Model-based Symmetric Information Theoretic Large Deformation

(A) Diffusion-tensor imaging assessment of white matter damage after traumatic brain injury (TBI). (A) Diffusion-tensor imaging assessment of white matter.

Neuroimaging Schizophrenia and Related Disorders

DTI Course Bring your computer and you will be guided through the steps to reconstruct the white matter fibers of the brain, inspect fibers reaching gray.

Association between white matter integrity and global neurocognitive impairment. Association between white matter integrity and global neurocognitive impairment.

Utah Algorithms Progress and Future Work

Figure 2 Brain MRI at 1 year of age

Brain miswiring in tuberous sclerosis and autism.

Arjun Watane Soumyabrata Dey

Diffusion Tensor MRI of White Matter of Healthy Full-term Newborns: Relationship to Neurodevelopmental Outcomes Higher white matter integrity (as reflected.

Presentation transcript:

Statistical Analysis of Anatomy from Medical Images Tom Fletcher School of Computing University of Utah National Alliance for Medical Image Computing

What is an Average Brain? ? Simple, linear statistics do not capture anatomical shapeSimple, linear statistics do not capture anatomical shape Average anatomy has minimal deformation to input dataAverage anatomy has minimal deformation to input data Nonlinear statistics of shape are neededNonlinear statistics of shape are needed

What is an Average Brain? Intensity Average Shape Average

Combining Structural and Diffusion Tensor MRI

Regression of FA Genu of the Corpus Callosum (CC) Raw Data Scatterplot Regression w/ std dev

Language Networks in Autism A key diagnostic feature of autism is impairment in communicationA key diagnostic feature of autism is impairment in communication The arcuate fasciculus is a white matter tract crucial in languageThe arcuate fasciculus is a white matter tract crucial in language Is the arcuate fasciculus different in autism?Is the arcuate fasciculus different in autism?

Arcuate Fasciculus

Arcuate Analysis in Autism

How Does the Brain Change in Normal Aging? Infer structure of the brain as a function of age from a population (3D MRI)Infer structure of the brain as a function of age from a population (3D MRI) Regression of brain shapeRegression of brain shape Healthy adult brain image database (Bullitt) ~100 healthy adults ages 20-80Healthy adult brain image database (Bullitt) ~100 healthy adults ages 20-80

Regression of Scalar Data

Kernel Regression of Deformable Images Davis, Fletcher, Bullit, Joshi, ICCV 2008: Marr Prize (Best Paper)

Regression of Aging Brain

Acknowledgements Collaborators:Collaborators: –SCI Institute: Ross Whitaker, Sarang Joshi, Guido Gerig –Utah Autism: Janet Lainhart, Molly Dubray –Aging: Liz Bullitt (UNC) Students:Students: –Ran Tao, Saurav Basu, Won-Ki Jeong, Brad Davis Funding:Funding: –NA-MIC: NIH Grant U54 EB –Autism Speaks Fellowship