Automatic Registration-Based Segmentation for ... - NeoBrainS12

Automatic Registration-Based Segmentation for
Neonatal Brains Using ANTs and Atropos
Jue Wu and Brian Avants
Penn Image Computing and Science Lab,
University of Pennsylvania, Philadelphia, USA
Abstract. We implemented an automatic open-source processing pipeline
for neonatal brain tissue segmentation. The framework makes use of N4
to correct bias field, the deformable registration SyN to warp a public
neonatal template to the test image, and multivariate MRF-based segmentation
Atropos to perform segmentation. The pipeline does not need
training and runs efficiently. It can achieve satisfactory results for most
classes of a neonatal brain MR image.
1 A Prior-Based Segmentation Algorithm
We contribute a template-based method for the challenging problem of tissue
segmentation in the neonatal brain. The task is difficult because of intensity
inversion of unmyelinated white matter and low resolution (or large slice thickness)
in standard neonatal T1 or T2-weighted MRI. Our framework employs SyN
diffeomorphic registration and Atropos prior-based multivariate segmentation
methods available within the open-source, multi-platform Advanced Normalization
Tools (ANTs). SyN and Atropos are general-purpose tools, which were not
designed specifically for neonatal brain processing. However, with the assistance
of a neonatal brain template, the general purpose methods are adapted for a
task-specific algorithm.
There are four components in the proposed framework: registration, bias
correction, segmentation and post-processing (see Fig. 1).
1.1 Registration
Assuming there is a proper template available for each test image to be segmented,
we aim to align the template with the test image and thus transform
the priors associated with the template to the subject space. The registration
is performed via the diffeomorphic mapping methods available within ANTs
software [1], which rank among top performers in terms of registration accuracy
[2]. ANTs is freely available at http://picsl.upenn.edu/ANTS/download.php. We
use the neighborhood cross correlation as similarity measure for the mono-modal
registration and mutual information for intra-subject affine registration between
T1 and T2 weighted MR images. Registration between T1 and T2 weighted
modalities enables us to employ a multivariate data-term in the segmentation

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Fig. 1. Overview of the proposed algorithm
formulation. The brain mask from the template was transformed to subject space
and helped remove non-brain tissues.
The line of code for registration between template and test image is:
ANTS 3 -m CC[t2template-GA.nii.gz, test_image.nii.gz, 1, 2]
-r Gauss[3,0] -t SyN[0.25] -i 90x100x80 -o output.nii.gz
1.2 Template Used
To a large extent, the success of prior-based segmentation hinges on the quality
of the chosen template and the similarity between template and test image. We
chose a set of premature neonatal T2 templates from Imperial College London,
which have high definition and good tissue contrast with various post-menstrual
ages. These atlases are based on 204 preterm babies with no observed obvious
pathology and freely available at http://www.brain-development.org/. We
picked two templates, one 30 and the other 40 week old, for the current processing.
The original templates have no label for myelinated WM and no separated
labels for ventricular CSF and extra-cerebral CSF so we created the labels manually
according to the Challenge’s protocol.
1.3 Pre-processing
Pre-processing before segmentation only involved MR field inhomogeneity correction.
We employed the N4 method [3] to correct bias field and used the probabilistic
map of white matter as a reference. Bias field correction was done for
independently for both T1 and T2 images.

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1.4 Segmentation
Tissue classification is accomplished by Atropos which uses expectation maximization
to optimize a probabilistic formulation that links a data term with both
spatial and Markov Random Field (MRF) priors. The posterior probability in
the MRF model is composed of the likelihood probability for voxel intensity,
prior probability for smoothness constraint and template priors. Optimization
is achieved by expectation maximization with an iterated conditional modes parameter
update schema. For technical details about Atropos, readers are referred
to the work of Avants et al. [3]
The Atropos method takes prior probability maps of all tissue classes and
the T2 image (plus the T1 for the final iteration) as input. It outputs the segmentation
of all classes as well as probability maps for all classes. We restarted
this process three times by setting the output probability maps of the previous
iteration as input probability maps of the next iteration. Due to close intensity
profiles between several tissues (such as cortex and deep gray matter, white matter
and cerebellum, ventricular CSF and extra-cerebral CSF), the tissue priors
were recombined with output probability maps between two iterations to reinforce
spatial constraint. Further details are available in the scripts used for this
study which are available from the authors.
The line of code for one iteration of prior-based segmentation:
Atropos -d 3 -x mask.nii -c [2,1.e-9] -m [0.10,1x1x1] -u 0 --icm [1,1]
-a test_t1.nii -a test_t2.nii -o [seg.nii.gz,segProb%02d.nii.gz]
-i PriorProbabilityImages[8,prior%02d.nii.gz,0.5] -p Socrates[0]
1.5 Post-processing
The purpose of post-processing is to decrease the number of misclassified voxels
due to unusual intensity of these voxels. The MRF smoothness constraint may
not be able to remove them because they can appear in a cluster. These clusters
can be reduced by reimposing the template-based priors, which are likely to show
low probability of the tissue at this location. Therefore the misclassification was
replaced with higher probability tissue class.
1.6 Strengths and Limitations
The proposed algorithm has several advantages.
No training is needed. The algorithm only needs a labeled template, where
prior knowledge of tissue distribution is incorporated, and thus training examples
are not necessary.
Relatively fast to run. For each subject, the registration unit takes 40 to 60
minutes depending on the size of the brain. The segmentation part takes about
20 minutes. Bias correction is about 20 minutes and post-processing lasts lest
than 1 minute. The computation was done on a Linux platform with Intel Xeon
3.2GHz CPU and 2GB memory.

4 Jue Wu and Brian Avants
Open source and public templates. The implementation is based on opensource
softwares and we used templates freely available to the public. Therefore
the proposed method should have good reproducibility and can be made available
to the public in the future.
One significant limitation of the proposed method is that the extra-cerebral
CSF segmentation is suboptimal due to the fact that the template did not have
features outside of the cerebrum (skull, dura, etc) whereas the test images cover
the whole head. While our registration methodology is fairly robust, in some
cases, this difference may be significant enough to impair registration performance.
In such cases, the segmentation will also suffer. It is also possible that
neither smoothness prior and template prior can eliminate all misclassification.
Additional anatomical constraints may be helpful, for example, WM and extracerebral
CSF voxels cannot be neighbors.
2 Evaluation Results
The snapshots for some example segmentation are shown in Fig. 2. The quantitative
evaluation results from the Challenge are shown in Fig. 3, 4 and 5.
Fig. 2. First row is one slice of the first subject from each set (40wk axial, 30wk coronal,
40 wk coronal. Second row is the corresponding segmentation.

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Fig. 3. Quantitative results for set1

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Fig. 4. Quantitative results for set2

Title Suppressed Due to Excessive Length 7
Fig. 5. Quantitative results for set3

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References
1. Avants, B., Epstein, C., Grossman, M., Gee, J.: Symmetric diffeomorphic image
registration with cross-correlation: Evaluating automated labeling of elderly and
neurodegenerative brain. Medical Image Analysis 12 (2008) 26–41
2. Klein, A., Andersson, J., Ardekani, B., Ashburner, J., Avants, B., Chiang, M., Christensen,
G., Collins, D., Gee, J., Hellier, P., Hyun, S., Jenkinson, M., Lepage, C.,
Rueckert, D., Thompson, P., Vercauteren, T., Woods, R., Mann, J., Parsey, R.:
Evaluation of 14 nonlinear deformation algorithms applied to human brain mri registration.
NeuroImage 46 (2009) 786–802
3. Avants, B., Tustison, N., Wu, J., Cook, P., Gee, J.: An open source multivariate
framework for n-tissue segmentation evaluation on public data. Neuroinformatics 9
(2011) 381–400