quantitative brain structure analysis on mr images zuyao shan, ph.d. division of translational...
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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