Rapid advancement in biological and medical imaging technologies increases demand for quantitative, computational anatomy tools. The principal tools of this emerging field are deformable mappings between images whether they be driven by similarity metrics which are intensity-based, point-set-based, or both. Several categories of mappings exist in the literature. Of particular recent interest are diffeomorphic transformations which, by definition, preserve topology. Topology preservation is fundamental to making comparisons between objects in the natural world that are thought to differ or change while preserving local neighborhood relations. Cytoarchitectonic brain mapping studies also suggest that the layout of cell types throughout the brain is generally preserved (Schleicher et al., 2009), further motivating diffeomorphic mapping in the context of the brain.
Our limited assessment of published research mirrors the experience of many others who prefer a working paradigm of reproducible research (Kovacevic, 2006). Dr. Kovacevic describes “[reproducible research as] the idea that, in ‘computational’ sciences, the ultimate product is not a published paper but, rather, the entire environment used to produce the results in the paper (data, software,etc.).” After an informal survey of 15 published papers, she finds “none had code available” and “in only about half the cases were the parameters [of the algorithm] specified.” The computational sciences research community also voices concerns about reproducibility (Yoo and Metaxas, 2005; Ibanez et al., 2006). In this paper, we discuss our contribution to the open source medical image analysis research community which we call ANTs (Advanced Neuroimaging Tools). Built on an an Insight ToolKit (ITK) framework, this software package comprises a suite of tools for image registration, template building and segmentation based on previously published research. Here, we provide an overview of the package and detail recent technical advances, in the spirit of previous papers published in this journal (Neu et al., 2005; Zhang et al., 2008; Patel et al., 2010) and open source registration tools such as Elastix (Klein et al., 2010b).
The recent outcome from two large-scale comparative image registration algorithm assessments (Klein et al., 2009), http://empire10.isi.uu.nl is perhaps the most persuasive evidence motivating the contributions discussed in this paper. Our Symmetric Normalization (SyN) transformation model (Avants et al., 2008) performs consistently in the top rank across all tests in the Klein et al. (2009) study and finished first overall in the phase one Empire-10 evaluation study of intra-subject thoracic CT registration (http://empire10.isi.uu.nl). Unlike some of the other algorithms in these studies, all of our methods (not just SyN) are open source software.
One difficulty in interpreting the results of these evaluation studies is that each algorithm uses a different combination of transformation model (the geometric constraint on the mapping between brains), similarity metric (the measure that evaluates how similar two images appear), and multi-resolution, optimization, and resampling strategies and parameter settings. Thus, one cannot isolate the effect of transformation model from similarity metric or optimization strategy. Other aspects of implementation may also differ, including whether the authors recommend using whole head or whole brain data. For instance, the DARTEL algorithm (Ashburner, 2007) uses whole head data and segmentation to aid performance while the other methods did not incorporate segmentation. The follow-up evaluation study Klein et al. (2010a) evaluated ART2.0 (Ardekani et al., 2005), SyN, and Freesurfer (Fischl and Dale, 2000) on whole head data and found that both brain extraction and registration via an “optimal” (group-generated) template improve performance. However, Klein et al. (2010a) applied generic parameters for ANTs, including the similarity metric, which might have resulted in suboptimal performance for the whole head component of the study.
Consequently, here we study the effect of the similarity metric on whole head registration-based labeling via an optimal template. We evaluate ANTs affine as well as nonlinear registration performance because accuracy in both stages is critical for successful registration-based brain segmentation/labeling. Furthermore, this problem is faced routinely in brain image processing labs (Ségonne et al., 2004; Sadananthan et al., 2010; Park and Lee, 2009; Lim and Pfefferbaum, 1989; de Boer et al., 2010; Acosta-Cabronero et al., 2008). One advantage of a consistent and modular framework, such as constructed in ANTs, is that it is possible to evaluate a single component of the processing stream while holding all other aspects constant.
The paper organization: Section 2 gives an overview of the transformation models and similarity metrics in ANTs and their use with SyN in population mapping. Section 3 reports results on a series of large-scale experiments using the LPBA manually labeled dataset to evaluate ANTs registration applied to cortical and brain labeling. Finally, we close with a discussion of our findings.