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Brain AtlasesEpidaure-LONI Associated teams

2002-2004

X. Pennec, N. Ayache, A. Pitiot, V. Arsigny, P. Fillard

P. Thompson, A. Toga, J. Annese

EPIDAURE Project

2004, route des Lucioles B.P. 93

06902 Sophia Antipolis Cedex (France)

LONI

Laboratory of Neuro Imaging

UCLA

California, USA

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Epidaure LONI Associated teams on Brain Atlases

Functional and structural analysis of the human brain High variability of the shape of structures High variability of functions localization High number of relevant variables (age, sex, pathologies…)

Building Statistical atlases requires Powerful algorithms Large datasets (several hundreds)

Complementarity of the teams Epidaure: Methodology of image registration/segmentation LONI: neuroanatomical experts,

development/exploitation of large international databases

EPIDAURE LONI

Atlas

MRI Histology

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Brain Atlas: Original objectives

Capitalize on the join expertise for more powerful atlases Algorithms

Registration / segmentation tools

Databases MRI, Spect, Histology

Evaluation / validation of the methodologies

Exchange researchers PhD Co-supervision (A. Pitiot) Annual visits

P. Thompson, A. Toga, N. Ayache, H. Delingette, X. Pennec Workshop on Brain Atlases

summer school on Computational Anatomy

EPIDAURE

LONI

LONI EPIDAURE

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LONI at UCLA

The LONI team TO BE DEVELOPPED

The coordinator: Paul Thompson (To be summurized) Batchelor 1991, Master 1993, Oxford Univ. PhD in Neuroscience 1998, UCLA. Assistant professor at UCLA since 1994

Publications 200 refereed publication (1996-2004) (Nature Neuroscience, Nature

genetics, TMI, MedIA, J. Neurosciences, NeuroImage…) Assoc. editor of Human Brain Mapping, Editorial board of MedIA…

Research interests: Human brain mapping (mathematical and computer intensive methods,

building of atlases, encoding variability…) Brain pathologies (Alzheimer, Schizaphrenia, neuro-oncology) Barin development (pediatric images)

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Quick History

Scientific collaboration initiated in 1999 Evaluation of histological slices registration (Rat brain images fro LONI)

S. Ourselin et al., “Reconstructing a 3D Structure from Serial Histological Sections”, IVC 2000.

2 Chapters in “Brain Warping”, A.W. Toga editor, 2003 J.-P. Thirion, “Diffusing Models and Applications” G. Subsol, “Crest Lines for Curve Based Warping”

Co-supervision of Alain Pitiot’s PhD (started sept. 2000) Alain Pitiot, “Segmentation automatique de structures cerebrales s’appuyant sur des connaissances

explicites”, Ecole des Mines de Paris, Nov. 2003.

Leveraging by the asociated team program (2002-2003) 5 peer reviewed publications co-authored by both teams Software exchanges (10 UCLA publication co-authored by A. Pitiot) Data exchanges (Arsigny, prize at MICCAI, Media)

New collaborations on brain variability modeling (summer 2003-2005++) PhD Vincent Arsigny: matching sulcal lines (summer 2003, in preparation) PhD Pierre Fillard (started sept 2004): article IPMI’05, MICCAI’05

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Presentation Overview

To be completed

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PhD Alain PitiotAutomatic segmentation of brain structure using explicit knowledge

Automated segmentation system

maximum a priori knowledge

deformable models

explicit information

Hybrid MRI/histology atlas

MRI: in vivo, macroscopic

histology: post mortem, microscopic

3-D reconstruction

3-D M

RI w

ith superimposed

segmented structures

3-D histological volum

e

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Composite segmentation systemn

eura

l te

xtu

re fi

lterin

gp

iecew

ise a

ffine re

gistration

knowledge-driven segmentation

learnt shape models

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Neural Texture Filtering

Texture classification

neural classifier

texture maps

Hybrid neural architecture

hybrid neural architecture

classification results

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Statistical Shape Modeling

Objective

statistical shape analysis

PCA on shapes

Learning approach to reparameterization

introduction of explicit knowledge

shape distance matrix observed transport shape measure

shape modes of variation

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Knowledge-driven Segmentation

Objective

• segmentation of anatomical structures

• mix bottom-up constraints with

top-down medical knowledge

explicit medical information

Rules

• escape local minima

• dynamic control

• meta-rules (error checking)segmentation results

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Piecewise Affine Registration

Motivation

• registration of 2-D biological images

• adapted transformation model:

piecewise affine

• specific similarity measure:

constrained correlation coefficient

Method

• similarity map

• hierarchical clustering

• hybrid elastic/affine interpolation

registration results

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wcci-ijcnn ’03hbm’02journal of anatomy

ipmi’03hbm’01,’02,’03neuroimage 2003

miccai’03

tmi 2002

wbir’03tmi 2003

Joint Contributions

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Executive summary 2002-2003

Exchange of researchers PhD Alain Pitiot: joint supervision and localization (50% each) Visit of N. Ayache and H. Delingette at UCLA (Dec. 2001) Visit of P. Thompson at Sophia (Nov 2003, PhD defense)

Exchange of software Reparameterization techniques of Pitiot (IPMI’03)

Visual cortex project (Annese HBM’03, Neuroimage 04) Robust registration software Baladin included in the LONI Pipeline

(MAP: Mouse brain Atlas, J. Annese, Visual Cortex, S. Ying, MD, Cerebellum).

Exchange of Data Neuroanatomical expertise Segmentations of 4 structures in Brain MR (Pitiot MICCAI’03) Histological data from J. Annese (Pitiot WBIR’03, Arsigny MICCAI’03) Segmented Brain sulci (V. Arsigny ++)

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Presentation Overview

To be completed

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A unique database Acquired during 10 years of

participation to international projects More than 500 subjects 72 manually delineated sulcal lines

(roots of grooves on the surface of the brain cortex)

MRI images Normal and pathological data Medical annotations

Potentially new anatomical findings

Morphometry of Sucal Lines (summer 2003-2005++)

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Innovative methods to model the brain variability Learn local brain variability from sulci

Learn global correlations in variability and link with asymetry

Better constrain inter-subject registration

Correlate this variability with age, pathologies Understanding of neurological diseases (Alzheimer, schizophrénie...)

Early diagnosis, follow-up studies

Morphometry of Sucal Lines (summer 2003-2005++)

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Objectifs de l’étude

Description fine de la variabilité des lignes sulcales grâce à des techniques d ’analyse innovantes

Obtention de nouvelles connaissances anatomiques : corrélations morphologiques entre sillons, asymétrie cérébrale ...

• Application : aide au diagnostic, meilleure compréhension de maladies neurologiques (Alzheimer, schizophrénie...)• Etiquetage automatique des lignes sulcales

Données anatomiques (vert-jaune) etlignes moyennes (rouge)

Variance le long des lignes moyennes. Rouge: faible, bleu élevée:

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Computation of Average Sulci

red : mean curve green et yellow : ~80 instances of

72 sulci

Alternate minimization of global variance Dynamic programming to match the mean to instances Gradient descent to compute the mean curve position

Arsigny et al. 2004, to appearSylvius Fissure

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Anatomical variability

Variance along the mean sulci Red (low) to blue (high)

Arsigny et al. 2004, to appear

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Extraction of Covariance Tensors

Covariance Tensors along Sylvius Fissure

Currently:

80 instances of 72 sulci

About 1250 tensors

Fillard, Pennec, Ayache, Thompson, 2004, to appear

Color codes Trace

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Compressed Tensor Representation

Mean sulcal line + 4 covariance matrices optimize for the 4 most representative tensors Interpolation in-between, extrapolation outside (removes outliers)

Sylvian fissure

The 4 most representative tensors.

Interpolation from the 4 tensors.

Raw estimation

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Compressed Tensor Representation

Representative Tensors (250) Reconstructed Tensors (1250) (Riemannian Interpolation)

Fillard-Pennec-Ayache-Thompson 2004, to appear

Original Tensors (~ 1250)

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Variability Tensors

Color codes tensor trace

Fillard-Pennec-Ayache-Thompson 2004, to appear

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Asymmetry Measure

Color Codes Distance between “symmetric” tensors 22/1'2/1''2'

2

)..log(|),(L

dist

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Full Brain extrapolation of the variability

Color code: principal eigenvector (red: left-right, green: posterior-anterior, blue:

inferior-superior)

Color code: trace

Anterior view

Fillard-Pennec-Thompson- Ayache 2004, to appear

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Color code: principal

eigenvector

Color code: trace

Full Brain extrapolation of the variability

Fillard-Pennec-Thompson- Ayache 2004, to appear

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Scientific results 2004

Leveraging the Theory General framework for computing on tensor fields Interpolation, diffusion, filtering…

“Side results” in Diffusion tensor imaging Regularization for fiber tracts estimation Registration,...

Variability of the brain Learn Variability from Large Group Studies Statistical Comparisons between Groups Exploit Variability to Improve Inter-Subject Registration

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Executive summary 2004

Important scientific results

IPAM summer school on Computational Anatomy 1 week, July 2004, organized by P. Thompson

+ 1 week on functional brain imaging Participation of N. Ayache (Invited speaker), X. Pennec,

P. Fillard, and members of Odyssee (O. Faugeras, 2 PhDs).

Tutorial at MICCAI 2004 on evolving processes in Med. Images Organized by N. Ayache, P. Thompson speaker Visit of P. Thompson at Sophia afterward

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Work plan for 2005 ++

Modeling the brain variability (PhD P. Fillard) Validation of the developed models Learn the full Green’s function Cov(x,y) for all x,y Theoretical tools already available

Better constrains inter-subject registration (PhD V. Arsigny) Non stationnarity OK (R. Stefanescu) Extend to long distnace correlations

Investigate GRID aspects (PhD. T. Glatard) LONI Pipeline, BIRN

Summer school on Computation Anatomy in Sophia-Antipolis in 2006

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Planned budget (2005)

Salaries PhD Fellowship “complement” for P. Fillard Visit P. Thompson at Sophia

Missions Long stay of P. Fillard at LONI

A budget is planned at LONI for complementing the salary

Conferences + visits at UCLA: IPMI’05 (Glenwood Springs, Colorado): X. Pennec + P. Fillard MICCAI’05 (Palm Springs, CA): N. Ayache, V. Arsigny, X. Pennec

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Conclusion

Very active collaboration (software, data, publications)

Access to a unique database (acquision cost > 1 M$)

Potentially new findings in neuro-anatomy and in some brain pathologies