Transcript 3D Segmentation of Rodent of Brain Structures Using Active

3D Segmentation of Rodent Brain Structures Using
Active Volume Model With Shape Priors
Shaoting Zhang1, Junzhou Huang1, Mustafa Uzunbas1, Tian Shen2, Foteini
Delis3, Xiaolei Huang2, Nora Volkow3, Panayotis Thanos3, Dimitris Metaxas1
1
2
CBIM, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
Computer Science and Engineering Department, Lehigh University, PA, USA
3 Brookhaven National Laboratory, NY, USA
Motivations
• Rodents are often used as
models of human disease.
• Use Magnetic Resonance
Microscopy (MRM) to get
3D image for rodent brain.
• 3D segmentation of brain
regions based on MR
images of the rodent brain.
• Deformable model based
segmentation.
Motivations
• Three challenges: 1) unclear boundary, 2)
complex textures, 3) complex shape.
Relevant work
• Deformable model based segmentation
– Deformable Models with Smoothness Constraints
• Active contour [M. Kass, IJCV’88]
• Gradient Vector Flow [C. Xu, TIP’98]
• Deformable Superquadrics and Metamorphs [Metaxas
91,92; Huang, 08]
– Priors from training data
• ASM [T.F. Cootes, CVIU’95]
• 3D ASM [Y. Zheng, TMI’08]
4
Proposed method
-Framework
Offline Learning
Training
Shapes
Geometry
Processing
Shape
Registration
PCA
Shape
Statistics
Runtime Segmentation System
Input
Image
Image
Alignment
Volumetric
Deformation
Shape
Constraint
Result
Proposed method
-Build Shape Statistics
• Geometry processing (decimation, detailpreserved smoothing)
Nealen, et.al.:
LMO,
GRAPHITE’06
Proposed method
-Build Shape Statistics
• Shape registration using AFDM
Shen, et.al.: AFDM, TMI’01
Proposed method
-Build Shape Statistics
• PCA analysis (mean and variance)
Cootes, et.al.: ASM, CVIU’95
Proposed method
-Deformation module
• Evolution of probability density function computed
from region information
Huang, et.al.: Metamorphs, PAMI’08
Proposed method
-Deformation module
• 3D Finite Element Method (A3D·V=LV)
Metaxas 92, Shen, et.al.: Active Volume Model, CVPR’09
Proposed method
-Deformation module
A3D (smoothness)
Sorkine, et.al.: Laplacian Mesh Processing, EG’05
Proposed method
-Framework, revisit
Initialization
Input
Image
Mean
Mesh
Image
Alignment
Initialization
Reference
Image
Shape
Statistics
3D Metamorphs
(AVM)
ASM Shape
Refinement
Deformation
Result
Experiments
• Settings
–
–
–
–
–
–
–
Adult male Sprague-Dawley rats
21.1T Bruker Biospin Avance scanner
FOV of 3.4 × 3.2 × 3.0mm, voxel size 0.08mm
Data: 2/3 training and 1/3 for testing
All normal cases
Segment the cerebellum, the left and right striatum.
C++ and Python2.6 and tested on a 2.40 GHz Intel
Core2 Quad computer with 8G RAM.
Experiments
• Cerebellum (complex texture and shape details)
Our method
No prior
Experiments
• Striatum (unobvious boundaries)
Our method
No prior
Experiments
• p: sensitivity; q: specificity; DSC: dice similarity
coefficient; RE-V: relative error of volume
magnitude.
2TP/(2TP+FP+FN)
TN/(TN+FP)
TP/(TP+FN)
Conclusions
• Proposed a segmentation framework using 3D
Metamorphs based deformation module and
ASM based shape prior module.
• It is particularly useful when there are a
limited number of training samples.
• In the future, we will test this algorithm on a
larger dataset and also investigate how to
segment multiple structures simultaneously
and effectively.
Thanks