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
2 Jue Wu and Brian Avants 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.
Title Suppressed Due to Excessive Length 3 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.
Title Suppressed Due to Excessive Length 5 Fig. 3. Quantitative results for set1
6 Jue Wu and Brian Avants Fig. 4. Quantitative results for set2
Title Suppressed Due to Excessive Length 7 Fig. 5. Quantitative results for set3
8 Jue Wu and Brian Avants 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