Li Wang1, Feng Shi1, Gang Li1, Weili Lin1, John H

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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

Content Introduction Proposed method Experimental results Discussion and conclusion

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

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

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

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

Comparison of subject-specific and population-based atlas

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

Step 3: level set segmentation

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.

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.

Leave-one-out cross validation on 20 subjects M V: Majority voting CLS (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.

Leave-one-out cross validation on 20 subjects M V: Majority voting CLS (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.

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

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

Images with different scanning parameters

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.

Source code can be found: http://www.unc.edu/~liwa Google: li wang unc