Quantitative Brain Structure Analysis on MR Images

Zuyao Shan, Ph.D.

Division of Translational Imaging ResearchDepartment of Radiological Sciences

Zuyao Shan, Ph.D.

Division of Translational Imaging ResearchDepartment of Radiological Sciences

Outline

• Introduction

• Cerebellum segmentation (Preliminary study)

• Cortical structure segmentation

• Introduction

• Cerebellum segmentation (Preliminary study)

• Cortical structure segmentation

Brain Segmentation

With the ability to identify brain structures on MR images and to detect anatomic changes, the new volumetric tools aid in the diagnosis, treatment, and elucidation of changes associated with disease or abnormality.

Registration – based approachesPros: Straightforward tenet, robustness Cons: Accuracy limited by match quality, mismatch leading to

significant errors, relying on image only. One-one mapping may not existed, Speed

Deformable model – based approachesPros: Prior knowledge incorporated, high accuracy.Cons: Good initialization needed, identification of landmarks

With the ability to identify brain structures on MR images and to detect anatomic changes, the new volumetric tools aid in the diagnosis, treatment, and elucidation of changes associated with disease or abnormality.

Registration – based approachesPros: Straightforward tenet, robustness Cons: Accuracy limited by match quality, mismatch leading to

significant errors, relying on image only. One-one mapping may not existed, Speed

Deformable model – based approachesPros: Prior knowledge incorporated, high accuracy.Cons: Good initialization needed, identification of landmarks

Brain Segmentation: inter-personal variability

More challenges in pediatric patients with brain tumors:

• Removal of tissues

• Different stages of development

An adequate method should cope with high inter-subject variability with high accuracy

Knowledge – guided active contour

Rigid-body registration: good initialization Prior defined template: Knowledge

incorporated Active contour adjustment: high accuracy,

robustness

Knowledge – guided active contour

Rigid-body registration: good initialization Prior defined template: Knowledge

incorporated Active contour adjustment: high accuracy,

robustness

Brain Segmentation: Cerebellum

Brain Segmentation: Cerebellum

Active contour (Snake): energy-minimizing spline

1 1

0 0( ( )) ( ( ))total internal externalE E v s ds E v s ds

( ) ( ( ), ( ))v s x s y s 0,1s

Brain Segmentation: Cerebellum

Active contour (Cont.): Internal energy22 2

2( ) ( )int

dv d vE = s s

ds ds

Small ( )v s

s

Tension in the contour, low internal energy

( )v s

s

High( )v s

s

Low

Small 2

2

( )v s

s

Bending in the contour, low internal energy

( )v s

s

High( )v s

s

Low

Brain Segmentation: Cerebellum

Active contour (Cont.): External energy

2( ) exp ( , )externalE s d x y

Sobel edge

detection

Distance

transform

Brain Segmentation: Cerebellum

Visual inspection

Brain Segmentation: Cerebellum

Visual inspection

Brain Segmentation: Cerebellum

Similarity evaluation

Kappa index

A vs. M1: ~ 0.94; A vs. M2: ~0.93; M1 vs. M2: 0.97

Compared with 0.77~0.84 for pediatric brain tumor patient in recent report1

S1∩ S2

1 21 2

1 2

2( , )

S SS S

S S

1. D’Haese P et al. Int J Radiat Oncol Biol Phys 2003; 57 (2 Suppl): S205

Brain Segmentation: Cortical Structures

New object functions

G H S In contrast, Registration – based approaches maximize S; deformable model – based approaches minimize H

Pediatric brain atlas

Affine registration (H)

3D active mesh (S)

KAM, Knowledge-guided Active Model

Brain Segmentation: Pediatric Brain Atlas

Brain Segmentation: Pediatric Brain Atlas

Brain Segmentation: Pediatric Brain Atlas

Brain Segmentation: Affine Registration

( ) ( ),

( , )

S A S IS

S A I

,

( , )( ) log , ( ) log , and ( , ) ( , ) log

( ) ( )a a i ii i a i

p a iS A p p S I p p S A I p a i

p a p i

12 DOF: 3 translations, 3 rotations, 3 scaling, and 3 shearing

Brain Segmentation: Active Models

2exp ( )ex

i

E d i

External Energy: attract triangle vertex to the edge of the image

Brain Segmentation: Active Models

Internal Energy: control the behavior of triangle mesh models

21 iij ij

jcur

ijij

s n n

ES

2cont ij i

i j

E d d

Brain Segmentation: Cortical Structures

Segmentation results

Brain Segmentation: Cortical Structures

Segmentation results

Brain Segmentation: Cortical Structures

Segmentation results compared with SPM2

Volumetric agreement: KAM : 95.4% ± 3.7%SPM2 : 90.4% ± 7.4%

Image similarities:KAM : 0.95 SPM2 : 0.86

Brain Segmentation: Summary

• Pediatric brain atlas

www.stjude.org/brainatlas

• KAM, Knowledge-guided Active Model

preliminary results indicate that when segmenting cortical structures, the KAM was in significantly better agreement with manually delineated structures than the nonlinear registration algorithm provided by SPM2.

Brain Segmentation: Future Studies

• Validation of KAM

• Application of KAM

Incorporating KAM into radiation therapy planning

Quantitative evaluation of cortical structure changes

• Further development of KAM

Subcortical Structures

Brain Tumors

Acknowledgements

Mentor: Dr. Wilburn E Reddick

Colleagues: Dr. Robert J Ogg

Dr. Fred H. Laningham

Dr. Claudia M. Hillenbrand

Carlos Parra, John Stagich, Dr. Qing Ji,

John Glass, Jinesh Jain, Travis Miller,

Rhonda Simmons