1. MIDAG@UNC September 27, 20041 / 18 Automatic Segmentation of Neonatal Brain MRI Marcel Prastawa 1, John Gilmore 2, Weili Lin 3, Guido Gerig 1,2 University of North Carolina at Chapel Hill 1 Department of Computer Science 2 Department of Psychiatry 3 Department of Radiology Partially supported by NIH Conte Center MH064065 and NIH-NIBIB R01 EB000219

2. MIDAG@UNC September 27, 2004Automatic Segmentation of Neonatal Brain MRI2 / 18 Goal Segmentation of brain tissues of newborn infants from multimodal MRI Particular interest in the developing white matter structure Motivation: Analysis of growth patterns, study of neuro- developmental disorders starting at a very early age gm mWM nWM csf

3. MIDAG@UNC September 27, 2004Automatic Segmentation of Neonatal Brain MRI3 / 18 Imaging the Developing Brain 35 weeks44 weeks15 months2 yearsadult age Rhesus equiv. 6yrs

4. MIDAG@UNC September 27, 2004Automatic Segmentation of Neonatal Brain MRI4 / 18 Challenges Smaller head size Low contrast-to-noise ratio Intensity inhomogeneity Motion artifacts Division of white matter into myelinated and non-myelinated regions Previous work: Warfield et al 1998 (methodology) Hüppi et al 1998 (clinical study) T1T2 T1 Labels gm myel. WM nonm. WM csf

5. MIDAG@UNC September 27, 2004Automatic Segmentation of Neonatal Brain MRI5 / 18 Challenges Neonate 2 weeks Adult CNR 2.9CNR 6.9

6. MIDAG@UNC September 27, 2004Automatic Segmentation of Neonatal Brain MRI6 / 18 Approach Non-optimal input data, rely on high level prior knowledge –Intensity ordering (e.g. in T2W) wm-myelinated < gm < wm-non-myelinated < csf –Aligned spatial priors (brain atlas) White matter is considered as one entity

7. MIDAG@UNC September 27, 2004Automatic Segmentation of Neonatal Brain MRI7 / 18 Method Overview Intensity Clustering Compute posteriors Compute PDFs Bias correction Whole brain MST clustering Parzen Windowing Initialization Segmentation - Bias correction Refinement

8. MIDAG@UNC September 27, 2004Automatic Segmentation of Neonatal Brain MRI8 / 18 Intensity Clustering Samples obtained by thresholding atlas priors T1 T2 Pr(wm, x) Overlay Noisy data, low contrast  robust techniques Two robust estimation techniques: –Minimum Spanning Tree (MST) clustering –Minimum Covariance Determinant (MCD) estimator Obtain initial estimates of intensity distributions

9. MIDAG@UNC September 27, 2004Automatic Segmentation of Neonatal Brain MRI9 / 18 [Cocosco et al 2003] Break long edges in MST, example: Detect multiple clusters while pruning outliers Iterative process, stops when cluster feature locations are in the desired order “Feature location” = summary value of cluster intensities Minimum Spanning Tree Clustering

10. MIDAG@UNC September 27, 2004Automatic Segmentation of Neonatal Brain MRI10 / 18 Determining Feature Locations Need reliable location estimate to find good clusters Standard estimates (e.g., mean, median) not always optimal Use robust estimator to determine location of a compact point set in a cluster MedianMean

11. MIDAG@UNC September 27, 2004Automatic Segmentation of Neonatal Brain MRI11 / 18 O = points used for estimation X = other data points Minimum Covariance Determinant [Rousseeuw et al 1999] Feature location of MST clusters to determine ordering? Smallest ellipsoid that covers at least half the data MCD gives robust location estimate Example:

12. MIDAG@UNC September 27, 2004Automatic Segmentation of Neonatal Brain MRI12 / 18 Intensity Clustering Algorithm Apply MCD to GM and CSF samples: obtain T2 locations Construct MST from WM samples T  2 Repeat until T = 1 Break edges longer than T x (local average length) Find largest myelinated WM cluster, where: T2 myel < T2 GM Find largest non-myelin. WM cluster, where: T2 GM < T2 non-myel < T2 CSF Stop if WM clusters found Otherwise, T  T – 0.01 T2 T1 gm mWM nWM csf

13. MIDAG@UNC September 27, 2004Automatic Segmentation of Neonatal Brain MRI13 / 18 Method Overview Intensity Clustering Compute posteriors Compute PDFs Bias correction Whole brain MST clustering Parzen Windowing Initial intensity Gaussian PDFs Initialization Segmentation - Bias correction Refinement

14. MIDAG@UNC September 27, 2004Automatic Segmentation of Neonatal Brain MRI14 / 18 Bias Correction [Wells et al 1996, van Leemput et al 1999] “Bias” = RF inhomogeneity and biology Images low contrast, histogram is smooth Use spatial context, bias is log-difference of input intensities and reconstructed “flat” image Fit polynomial to the bias field (weighted least squares) Interleaves segmentation and bias correction Compute posteriors Compute PDFs Bias correction Gaussian intensity PDFs bias biology

15. MIDAG@UNC September 27, 2004Automatic Segmentation of Neonatal Brain MRI15 / 18 Method Overview Intensity Clustering Compute posteriors Compute PDFs Bias correction Whole brain MST clustering Parzen Windowing Bias corrected images Segmentations Initialization Segmentation - Bias correction Refinement

16. MIDAG@UNC September 27, 2004Automatic Segmentation of Neonatal Brain MRI16 / 18 Refinement Previous stage assumes Gaussian intensity distributions May have non-optimal decision boundaries due to overlap Re-estimate intensity parameters from bias-corrected images –MST clustering to obtain training data –Parzen windowing to estimate density Parzen kernel density estimate Atlas prior

17. MIDAG@UNC September 27, 2004Automatic Segmentation of Neonatal Brain MRI17 / 18 Results [1/2] UNC Radiology Weili Lin (Siemens 3T head-only) UNC-0094 UNC-0096 T1T2 Classification 3D T1T2 Classification 3D

18. MIDAG@UNC September 27, 2004Automatic Segmentation of Neonatal Brain MRI18 / 18 Results [2/2] Provided by Petra Hüppi (Geneva, Philips 1.5T) Geneva-001 Geneva-002 T1 T2Classification 3D T1 T2Classification 3D

19. MIDAG@UNC September 27, 2004Automatic Segmentation of Neonatal Brain MRI19 / 18 Results: UNC 0096 Upper row: T1, T2w, Tissue labels, registered atlas Lower row: Probabilities for wm-myel, wm, gm, csf

20. MIDAG@UNC September 27, 2004Automatic Segmentation of Neonatal Brain MRI20 / 18 Results: UNC 0096

21. MIDAG@UNC September 27, 2004Automatic Segmentation of Neonatal Brain MRI21 / 18 Summary Automatic brain tissue segmentation of neonatal MRI Detects white matter as myelinated and non-myelinated structures Makes use of prior knowledge: –Image intensity ordering –Spatial locations (probabilistic atlas prior) To be used in two large UNC neonatal MRI studies –Silvio Conte Center: 125 neonates at risk –Neonate Twin study (heritability) Current focus: Validation

22. MIDAG@UNC September 27, 2004Automatic Segmentation of Neonatal Brain MRI22 / 18 Acknowledgements Elizabeth Bullitt Petra Hüppi Koen van Leemput Insight Toolkit Community Neoseg v1.0b

23. MIDAG@UNC September 27, 2004Automatic Segmentation of Neonatal Brain MRI23 / 18 Validation (in progress) A) Semiautomated expert segmentation of a few cases Edge-based segmentation Level-set evolution Manual editing Primarily: White-gray contour B) Simulated MRI data (similar to MNI ICBM)

24. MIDAG@UNC September 27, 2004Automatic Segmentation of Neonatal Brain MRI24 / 18 New Probabilistic Atlas for the 2yrs group 14 subjects, aligned, intensity adjusted, segmented (UNC M. Jomier/Piven/Cody/Gimpel/Gerig)