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Patch-driven Neonatal Brain MRI Segmentation with Sparse Representation and Level Sets

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,

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Patch-driven Neonatal Brain MRI Segmentation with Sparse Representation and Level Sets

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  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. 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. Proposed method Step 1 … Template images Testing subject Subject - 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 WM GM D:[ ] 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. 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.

  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., 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.

  13. 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.

  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: http://www.unc.edu/~liwa • Google: li wang unc

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