1. Local Semi-Supervised Approach to Brain Tissue Classification in Early Brain
Infant Development
Nataliya Portman, Paule-Joanne Toussaint and Alan C. Evans,
McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University
nataliyaportman@gmail.com, {paule.toussaint, alan.evans}@mcgill.ca
1. Introduction
Most segmentation methods in infant brain MRI rely on atlases derived from a small cohort that do
not capture full anatomical variability. To date, the NIH "Objective2” (O2) database of early brain
development does not possess a standard infant brain atlas with accurate measures of boundary
uncertainty for tissue classes [1].
In order to improve the accuracy of detection of grey matter, white matter, and CSF in early
developing brains in the absence of a "ground truth", a local classification method based on Kernel
Fisher Discriminant analysis (KFDA) for pattern recognition, combined with an objective structural
similarity index (SSIM) for quality assessment was proposed [2,3]. While it performs well for ages
8 months and older, it fails to accurately classify WM in regions with similar WM and GM (or WM
and CSF) T2 intensities for a younger age group before 5 months of age.
Here we further extend the KFDA-based framework for identification of myelinated and
unmyelinated WM in younger infant brains.
2. Dataset
Fig. 1. T1w, T2w and PDw templates for early
infant age ranges (in months) for the O2 MRI
data.
3. Methods
− =
4. Results for the infant brain template: age range: 02-05 months
Fig. 3. Myelinated WM pattern globally estimated from myelinated WM reference volume using
EM classification into the CSF, G+W M and myelinated WM: (a), (b), (c) T2w axial slice view (in
black), (d) 3D view.
Fig. 4. (a), (d) Unmyelinated WM references (T2w axial slices) with masked myelinated WM, (b),
(e) Partial Volume Estimation (PVE) and (c), (f) local KFDA classifications into unmyelinated WM
(light grey), GM (dark grey) and the CSF (white), (g) 3D view of GM, (h) Unmyelinated WM in the
anterior right quadrant, (i), (j) Left and right hemispheric views of the CSF.
5. Discussion and Conclusion
In this study we addressed a challenging problem of tissue separation in infant brain MRI under 5
months of age. We showed that such data can be handled in the local KFDA-based framework by
subsequent delineation of tissue classes and alteration of reference images for each tissue class.
For myelinated WM, the reference is the difference between PDw and T1w images. An EM
segmentation of this reference reveals the symmetry of the myelinated WM pattern along the
medial longitudinal fissure, and its formation within the brain stem, spreading into the cerebellum,
the fronto-temporal area, and the occipital lobe.
Initially designed to handle variable GM/WM tissue contrast, the method successfully extracted
structural information about unmyelinated WM and GM from a T2w reference volume initialized
with fuzzy GM/WM boundaries obtained by PVE.
The segmentation strategy presented here improves our understanding of neurodevelopmental
effects on underlying anatomical and functional changes in early infant brain. It should be
extensible to neonatal (and fetal) brains, and could find application in the detection of WM lesions
with faint differences from GM T1w intensities such as present in multiple sclerosis.
Literature
1. Almli, C.R. (2007):The NIH MRI study of normal brain development (Objective-2), NeuroImage 35(1).
2. Portman, N. (2015): "Local semi-supervised approach to brain tissue classification in child brain MRI", Submitted to NeuroImage.
3. Portman, N. (2013): Novel Vector-Valued Approach to Automatic Brain Tissue Classification, MCV-MICCAI2012 Proc., Springer LNCS, Vol. 7766.
4. Collins, D.L. (1999): ANIMAL+INSECT: Improved Cortical Structure Segmentation, IPMI LNCS, Vol. 1613.
5. Fonov, V. (2011): Unbiased average age-appropriate atlases for pediatric studies. NeuroImage , 54(1).
{Courtesy of V. Fonov, available online at at http://www.bic.mni.mcgill.ca/ServicesAtlases/NIHPD-obj2}
The NIH pediatric O2 MRI database is the
largest demographically diverse U.S. sample
that consists of 69 subjects aged 10 days to
4.5 years, scanned longitudinally. Data were
acquired on 1.5 T Siemens Sonata scanner
with 1×1×3 mm3 spatial resolution. O2 data
were corrected for image intensity non-
uniformity and registered to the MNI
stereotaxic space using ANIMAL, a spatial
normalization process [4]. The data were
resampled to 1 mm3 using tri-cubic
interpolation. T1w, T2w, PDw average
atlases were created for developmentally
important age ranges [5].In the proposed framework, we compute the structural closeness of classified 3D brain
subdomains comprised of GM, WM and CSF mean intensity value with their counterparts seen
in MR images.
MeanSSIM=0.8460
Fig. 2. (a) SSIM comparison of the classified temporal lobe region with T1w reference for ages 8
to 11 months, (b) Performance comparison of 3-class (previous) and 4-class (extended) regional
KFDA methods across slices of the O2 interior brain template (ages: 02-05 months), (c) T1w, (e)
T2w O2 template axial slices and the corresponding (d) 3-class and (f) 4-class segmentations.
Definition of a reference for each tissue type:
Myelinated WM: A difference between PDw and T1w images that enhances
myelinated regions shown in black
Unmyelinated WM and GM: T2w CSF: T1w
PDw T1w Reference
Other reference
slices
Myelinated
regions
Global delineation of myelinated
WM using Expectation-
Maximization (EM)
Optimal brain partitioning
into 3D brain subdomains
SSIM-guided KFDA detection of tissue classes in brain subvolumes
(1) CSF, input: (T1w, T2w) (2) unmyelinated WM and GM,
input: T2w with masked CSF
Myelinated WM has been masked
PVE initialization KFDA classification PVE initialization KFDA classification
Stitching of
classified
brain
subvolumes
using
Simulated
Annealing
(a) (b) (c)
(d) (e) (f)
(a) (b) (c)
(d)
(g)
(h)
(i) (j)
(c) (d)
(e) )
(a)
(b)
Total MSSIM
3-class 4-class
0.8245 0.8667