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Patch-driven Neonatal Brain MRI Segmentation with Sparse Representation and Level Sets. Li Wang 1 , Feng Shi 1 , Gang Li 1 , Weili Lin 1 , John H. Gilmore 2 , Dinggang Shen 1 1 Department of Radiology and BRIC, 2 Department of Psychiatry, - PowerPoint PPT Presentation
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Department of Radiology and BRIC, UNC-Chapel Hill
Patch-driven Neonatal Brain MRI Segmentation with Sparse Representation and Level Sets
Li Wang1, Feng Shi1, Gang Li1, Weili Lin1, John H. Gilmore2, Dinggang Shen1
1 Department of Radiology and BRIC, 2 Department of Psychiatry,
University of North Carolina at Chapel Hill, NC, USA
Department of Radiology and BRIC, UNC-Chapel Hill
Content
Introduction Proposed method Experimental results Discussion and conclusion
Department of Radiology and BRIC, UNC-Chapel Hill
Introduction
Accurate segmentation of neonatal brain MR images into WM, GM and CSF is essential in the study of infant brain development.
lower tissue contrast, severe partial volume effect, high image noise, and dynamic white matter myelination.
Neonatal image Adult image
Department of Radiology and BRIC, UNC-Chapel Hill
Introduction
Atlas-based Methods•Population-based atlas complex brain structures are generally diminished due to inter-subject anatomical variability
•Can we build a subject-specific atlas?
WM GM CSFOriginal
Department of Radiology and BRIC, UNC-Chapel Hill
Proposed method
Level setsegmentation
Testing subject Subject-specific atlas
Local spatial consistency
Final segmentation
Step 1
Step 2
Step 3
…
Template images
Department of Radiology and BRIC, UNC-Chapel Hill
Step1: Constructing a subject-specific atlas from population
X: D:[ ]WM GM CSF2 22
12 1 20
1min2 2
X D
α==
Testing subject
Template images
Department of Radiology and BRIC, UNC-Chapel Hill
Comparison of subject-specific and population-based atlas
Population-based atlas
Subject-specific atlas with spatial
consistency
Subject-specific atlas
Original T2 image
WM GM CSF
Department of Radiology and BRIC, UNC-Chapel Hill
Step2: local spatial consistency in the testing image space
2 2212 1 20
1min2 2
X D
Step 1: subject-specific atlas
Department of Radiology and BRIC, UNC-Chapel Hill
Step 3: level set segmentation
Department of Radiology and BRIC, UNC-Chapel Hill
Experimental results
Parameters selection
The weight for L1-term λ1=0.1, weight for L2-term λ2=0.01, patch size 5×5×5, local searching window 5×5×5.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36λ1 0.01 0.1 0.2 0.5 0.01 0.1 0.2 0.5 0.01 0.1 0.2 0.5 0.01 0.1 0.2 0.5 0.01 0.1 0.2 0.5 0.01 0.1 0.2 0.5 0.01 0.1 0.2 0.5 0.01 0.1 0.2 0.5 0.5 0.2 0.1 0.01w 3 3 3 3 3 3 3 3 3 3 3 3 5 5 5 5 5 5 5 5 5 5 5 5 7 7 7 7 7 7 7 7 7 7 7 7
wp 3 3 3 3 5 5 5 5 7 7 7 7 3 3 3 3 5 5 5 5 7 7 7 7 3 3 3 3 5 5 5 5 7 7 7 7WM+GM 1.78 1.79 1.79 1.78 1.77 1.79 1.79 1.78 1.75 1.78 1.78 1.77 1.81 1.82 1.82 1.81 1.81 1.82 1.82 1.81 1.81 1.82 1.83 1.81 1.8 1.8 1.8 1.78 1.81 1.81 1.81 1.79 1.8 1.82 1.82 1.82
1.73
1.75
1.77
1.79
1.81
1.83
Sum
Dic
e ra
tios
of W
M a
nd G
M
Department of Radiology and BRIC, UNC-Chapel Hill
Template numbers?
Box-whisker plots of Dice ratio of segmentation using an increasing number oftemplates from the library. Experiment is performed by leave-one-out using the library of 20 templates.
How many template images are needed to generate a good segmentation?
Department of Radiology and BRIC, UNC-Chapel Hill
Leave-one-out cross validation on 20 subjects
0.65
0.67
0.69
0.71
0.73
0.75
0.77
0.79
0.81
0.83
0.85
0.87
0.89
0.91
0.93
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Dice
ratio
Subject
WM
MVCLSCPMSubject-specific-atlasProposed (without spatial consistency)Proposed (with spatial consistency)
M V: Majority votingCLS (Coupled level sets): Wang, L., et al., 2011. Automatic segmentation of neonatal images using convex optimization and coupled level sets. NeuroImage 58, 805-817.CPM (Conventional patch-based method): Coupe, P.,et al., 2011. Patch-based segmentation using expert priors: Application to hippocampus and ventricle segmentation. NeuroImage 54, 940-954.
Department of Radiology and BRIC, UNC-Chapel Hill
Leave-one-out cross validation on 20 subjects
0.65
0.67
0.69
0.71
0.73
0.75
0.77
0.79
0.81
0.83
0.85
0.87
0.89
0.91
0.93
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Dice
ratio
Subject
GM
MVCLSCPMSubject-specific-atlasProposed (without spatial consistency)Proposed (with spatial consistency)
M V: Majority votingCLS (Coupled level sets): Wang, L., et al., 2011. Automatic segmentation of neonatal images using convex optimization and coupled level sets. NeuroImage 58, 805-817.CPM (Conventional patch-based method): Coupe, P.,et al., 2011. Patch-based segmentation using expert priors: Application to hippocampus and ventricle segmentation. NeuroImage 54, 940-954.
Department of Radiology and BRIC, UNC-Chapel Hill
8 testing subjects with manual segmentations
0.8
0.82
0.84
0.86
0.88
0.9
0.92
0.94
WM GM
Dice
ratio CLS
CPM
Proposed
WM difference GM difference
CLS: Coupled level setCPM: Conventional patch-based method
(a) Original
(e) CLS (f) CPM (g) Proposed
(b) CLS (c) CPM (d) Proposed
(h) CLS (i) CPM (j) Proposed
Department of Radiology and BRIC, UNC-Chapel Hill
94 testing subjects for qualitative evaluation
Original CLS
CPM ProposedCLS: Coupled level setCPM: Conventional patch-based method
Original CLS
CPM Proposed
Department of Radiology and BRIC, UNC-Chapel Hill
Images with different scanning parameters
sequence #2 sequence #3 sequence #4
Department of Radiology and BRIC, UNC-Chapel Hill
Conclusion
In this paper, we proposed a novel patch-driven level sets method for neonatal brain MR image segmentation.
The average total computational time is around 120 mins for the segmentation of a 256×256×198 image with a spatial resolution of 1×1×1 mm3 on our linux server with 8 CPUs and 16G memory.
Our future work will include more representative subjects (normal/abnormal) as templates.
Department of Radiology and BRIC, UNC-Chapel Hill
Source code can be found: http://www.unc.edu/~liwa Google: li wang unc