1. 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

2. Content
Introduction
Proposed method
Experimental results
Discussion and conclusion

3. 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

4. Population-based atlas
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?
Original WM GM
CSF

5. Step 1 Step 2 Step 3 Proposed method … Testing subject Template images-
specific atlas
Step 2
Local spatial consistency
Step 3
Level set
segmentation
Final segmentation

6. Step1: Constructing a subject-specific atlas from population
Template images
Testing subject
D:[ ] WM GM
CSF X: α= =

7. Comparison of subject-specific and population-based atlas

8. Step2: local spatial consistency in the testing image space
Step 1: subject-specific atlas

9. Step 3: level set segmentation

10. Parameters selection Experimental results
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.

11. Template numbers?
How many template images are needed to generate a good segmentation?
Box-whisker plots of Dice ratio of segmentation using an increasing number of
templates from the library. Experiment is performed by leave-one-out using the library of 20 templates.

12. Leave-one-out cross validation on 20 subjects
M V: Majority voting
CLS (Coupled level sets): Wang, L., et al., Automatic segmentation of neonatal images using convex optimization and coupled level sets. NeuroImage 58,
CPM (Conventional patch-based method): Coupe, P.,et al., Patch-based segmentation using expert priors: Application to hippocampus and ventricle segmentation. NeuroImage 54,

13. Leave-one-out cross validation on 20 subjects
M V: Majority voting
CLS (Coupled level sets): Wang, L., et al., Automatic segmentation of neonatal images using convex optimization and coupled level sets. NeuroImage 58,
CPM (Conventional patch-based method): Coupe, P.,et al., Patch-based segmentation using expert priors: Application to hippocampus and ventricle segmentation. NeuroImage 54,

14. 8 testing subjects with manual segmentations
CLS: Coupled level set
CPM: Conventional patch-based method
WM difference
GM difference

15. 94 testing subjects for qualitative evaluation
Original
CLS
Original
CLS
CPM
Proposed
CPM
Proposed
CLS: Coupled level set
CPM: Conventional patch-based method

16. Images with different scanning parameters

17. 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.

18. Source code can be found:
Google: li wang unc