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

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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

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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

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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

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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