Previous work from our group demonstrated the use of multiple input atlases to a modified multi-atlas framework (MAGeT-Brain) to improve subject-based segmentation accuracy. Currently, segmentation of the striatum, globus pallidus and thalamus are generated from a single high-resolution and -contrast MRI atlas derived from annotated serial histological sections. Here, we warp this atlas to five high-resolution MRI templates to create five de novo atlases. The overall goal of this work is to use these newly warped atlases as input to MAGeT-Brain in an effort to consolidate and improve the workflow presented in previous manuscripts from our group, allowing for simultaneous multi-structure segmentation. The work presented details the methodology used for the creation of the atlases using a technique previously proposed, where atlas labels are modified to mimic the intensity and contrast profile of MRI to facilitate atlas-to-template nonlinear transformation estimation. Dice’s Kappa metric was used to demonstrate high quality registration and segmentation accuracy of the atlases. The final atlases are available at https://github.com/CobraLab/atlases/tree/master/5-atlas-subcortical.
Machine-accessible metadata file describing the reported data (ISA-tab format)
Background and Summary
The subcortical structures of the brain, such as the striatum and thalamus, are positioned deep below the cortical manifold and serve as important network hubs and relays. Amongst their many roles in brain function, these regions are critically involved in motor function, addiction, and mood regulation1,
The gold standard for neuroanatomical MR image segmentation is manual delineation by an expert human rater based on prior histological and functional data. However, with the availability of increasingly large MRI datasets13,
Given the proliferation of automated segmentation tools, a recent study by our group7 sought to establish the validity and reliability of segmentation methods in defining the striatum, globus pallidus, and thalamus using FreeSurfer17 (http://surfer.nmr.mgh.harvard.edu), FSL-FIRST18 (http://fsl.fmrib.ox.ac.uk), and MAGeT-Brain16,19 (https://github.com/CobraLab/MAGeTbrain). When comparing manually defined labels to labels generated using each segmentation tool on thirty subjects (15 patients with first episode psychosis and 15 controls), correlations between automated and manually segmented volumes were strongest for MAGeT-Brain7.
The current implementation of the MAGeT-Brain pipeline allows for subject-based segmentation of the hippocampal subfields16,20, hippocampal white matter21, cerebellar lobules22, and the striatum, globus pallidus, and thalamus23. For hippocampal and cerebellar segmentation, five atlases, created from manual segmentation on five high-resolution MRI templates, are used to derive subject-based segmentation20,
Moreover, many groups24,
In this paper, we detail the steps used to adapt an existing high-resolution atlas of the basal ganglia and thalamus23 to match five high-resolution MRIs using atlas-to-template warping techniques previously described by our group23,32. The creation and use of five de novo atlases of the striatum, globus pallidus and thalamus, along with three-dimensional surface rendering of these structures will serve as a new set of priors for input into an existing multi-atlas segmentation pipeline, MAGeT-Brain19, with the primary benefit of homogenizing and improving upon its current implementation. MAGeT-Brain with the five newly-created input atlases will provide improved subject-based automated identification and estimation of volume and shape of striatum, globus pallidus and thalamus, and allow for the investigation of subcortical morphology in various subject groups ranging from healthy individuals to patient populations.
Atlas-to-template warping technique
We present a continuation of work previously presented in Chakravarty et al. (2006), which describes the creation of an atlas of the basal ganglia and thalamus derived from serial histological data, detailing 108 structures23. The brain used to create the histological data set was acquired in 1957 from a male patient who died of non-neurological causes at the Montreal Neurological Institute/Hospital, in which is intensively studied and used for teaching over the past 45 years. The histological images were manually segmented and labeled combining information and nomenclature using three different references33,
High-resolution T1-weighted images used for the creation of five subcortical atlases were acquired from 5 healthy subjects (2 male, 3 female, aged 29–57, average age of 37 years)20. All images were acquired on a 3 T GE Discovery MR 750 system (General Electric, Milwaukee, WI) at the Centre for Addiction and Mental Health (CAMH) in Toronto, Canada using an 8-channel head coil. Three sets of high-resolution T1-weighted images were acquired from each subject. T1-weighted images were acquired using the 3D inversion-prepared fast spoiled gradient-recalled echo acquisition (FSPGR-BRAVO; TE/TR=4.3 ms/9.2 ms, TI=650 ms, α=8°, 2NEX, FOV=22 cm, slice thickness=0.6 mm, 384×384 in-plane steps) with an isotropic voxel size of 0.6 mm. A final isotropic voxel size of 0.3 mm was obtained using zero-filling reconstruction filters, ZIPX2 and ZIP512, done on the GE scanner. For each subject, the three T1-weighted images were each corrected for RF inhomogeneity non-uniformity39 and normalized to a fixed intensity range (0–10,000) on a voxel-by-voxel basis to enhance signal and contrast40 to ultimately produce one final T1-weighted image volume. The final T1-weighted image volume was produced from the average of the three corrected MR images for each subject using a rigid-body alignment41. All images were converted to the MINC file format and subsequent image processing and neuroanatomical labeling was performed using tools from the MINC software distribution (http://www.bic.mni.mcgill.ca/ServicesSoftware/HomePage). The data acquisition was approved by the Centre for Addiciton and Mental Health Research Ethics Board, and all subjects provided written, informed consent for data acquisition and sharing. These templates were previously used by our group for the creation of atlases of the hippocampal subfields16,20, hippocampal white matter21, and the cerebellum22.
The atlas-to-template warping technique used here for the creation of five atlases was initiated with linear registration of the histologically-derived atlas to five T1-weighted high-resolution MRI templates20 (as described in Chakravarty et al. (2006)). By identifying homologous landmark-pairs, linear transformations are estimated to map the atlas to each MRI using a 12-parameter transformation (3 of each of translations, rotations, scales, and shears). After accounting for global differences with the linear registration, the remaining morphological differences between the atlas and the templates were accounted for using nonlinear registration to further refine the fit between the atlas and each template. However, the inherent differences in contrast and morphology presents a challenge for customization of the histology-derived atlas to MRI templates. To account for these differences, pseudo-MRIs were created by manually assigning an intensity value to each label value of the atlas based on the intensity of the matching structure in MRI template for which it is registered to. This remapping of the atlas labels to match the intensity profile of the templates allows the nonlinear registration algorithm to treat the atlas as a standard input MRI. Figure 1 details an overview of the atlas-to-template warping technique, from linear transform to pseudo-MRI to final fit.
The nonlinear registration was performed using the automatic nonlinear image matching and automatic labeling algorithm (ANIMAL)42,43 to register the pseudo-MRIs to the MRI templates. This nonlinear transformation is estimated in a hierarchical fashion, where large deformations are estimated using volumes blurred with a Gaussian kernel with a large full-width at half maximum (FWHM), which gets progressively smaller to refine deformations, to optimize a transformation that maximizes the similarity between the source and target image. The final nonlinear transformation is represented by a deformation field (defined by vectors spaced on a grid representing a three-dimensional translation at each node of the lattice grid) that is iteratively estimated in a two step process; the first step involves the calculation of local translations for each node that optimizes a local objective function (the correlation coefficient) and the second is a regularization step to ensure that the deformation field is continuous. The definition of the final deformation field produced by ANIMAL’s two-step algorithm is dependent upon the step size (the spacing between the lattice points of the grid), sublattice diameter (the diameter of a local spherical neighbourhood around each node in which the deformation is estimated) and the sublattice (number of nodes contained within a local spherical neighbourhood)32,40,42. Additionally, three regularization parameters are used to improve the quality of the nonlinear transformation estimated, where the weight parameter determines the proportion of each local translation estimated at each iteration that will be used at the next iteration, the stiffness parameter is the smoothing factor between iterations (to ensure continuity), and the similarity-cost ratio parameter balances the similarity between the volumes with the cost of the transformation32,40,42,43. The parameters used to calculate the atlas-to-template transformation using ANIMAL are given in Table 1. The final transformation was achieved through an optimization of a correlation coefficient objective function, and described in Collins & Evans (1997) and Chakravarty et al. (2006, 2008). Other nonlinear registration algorithms were attempted, and are discussed in section “Nonlinear Registration Algorithms”.
Atlas warping evaluation
The atlas-to-template warping technique presented in section “Atlas-to-template warping technique” was evaluated against manual segmentation of the striatum, globus pallidus, and thalamus to validate the accuracy of the atlas labels before using these atlases as input to the MAGeT-Brain pipeline.
Dice’s Kappa Overlap Metric
The labels defined in the atlas were compared to manual segmentation to determine the accuracy of the atlas-to-template warping technique. Dice similarity coefficient (also referred to as Dice's Kappa) (κ) metric is used here to evaluate the quality of the overlap of the atlas labels derived from the atlas-to-template warping technique with manual segmentation, as this segmentation method is the current gold-standard method. The Dice’s Kappa overlap metric score is determined by:
where a is the number of voxels common to both methods of segmentation, and b+c is the sum of the voxels uniquely labeled by each segmentation method. A higher Kappa value denotes a higher degree of overlap, where a score of 0 represents no overlap and a value of 1 represents perfect overlap, and scores greater than 0.7 are deemed acceptable in the segmentation literature32,44,
To evaluate the ‘‘goodness of fit” of atlas labels, segmentations of the striatum, globus pallidus, and thalamus, were generated using MAGeT-Brain segmentation19 using the current single input atlas by one of the authors (M.T.M.P.) to obtain labels on the five high-resolution T1 weighted images. Subsequently, these labels were manually corrected, particularly in regions where the endogenous contrast boundaries are not as distinct. These semi-manual segmentation were then used to evaluate segmentation accuracy of the atlas labels.
A single surface-based representation of the striatum, globus pallidus and thalamus are defined on a model atlas. The model was created using atlas-creation methods described previously47,
The surface representations of the striatum, globus pallidus and thalamus were created to obtain surface-based metrics, including surface area and displacement for shape analysis, using the MAGeT-Brain pipeline. This methodology is described in detail in previous work by our group6,7,16,31.
The atlases were created using tools from the MINC software distribution (http://www.bic.mni.mcgill.ca/ServicesSoftware/HomePage); however, NIfTI format (https://nifti.nimh.nih.gov/pub/dist/src/niftilib/nifti1.h) is also availble on our Figshare repository (Data Citation 1: Figshare https://dx.doi.org/10.6084/m9.figshare.c.4052768). The conversion from MINC to NIfTI-1 format was performed using mnc2nii (part of the MINCtools package: http://www.bic.mni.mcgill.ca/ServicesSoftware/HomePage).
The code for creation of the atlases is accessible through our Figshare repository (Data Citation 1: Figshare https://dx.doi.org/10.6084/m9.figshare.c.4052768). The ANIMAL script details the commands for the nonlinear registration step of the atlas-to-template warping technique. The resulting atlases contain 108 subcortical structures, all of the structures present in the histologically-derived atlas23 (Data Citation 1: Figshare https://dx.doi.org/10.6084/m9.figshare.c.4052768). The final atlases of the striatum, globus pallidus and thalamus, in which will be used as input atlases for the automated segmentation pipeline MAGeT-Brain, were generated using the mask script, also available in our Figshare repository (Data Citation 1: Figshare https://dx.doi.org/10.6084/m9.figshare.c.4052768).
MAGeT-Brain (https://github.com/CobraLab/MAGeTbrain), the automated segmentation pipeline, for which these atlases will be used as input to derive subject-based segmentation of the striatum, globus pallidus, and thalamus, is supported on the BIDS app (https://github.com/BIDS-Apps/MAGeTbrain) and OpenNeuro (reproducibility.stanford.edu/openneuro-app-highlights-maget-brain/), in addition to being available as an open source tool from our website (http://cobralab.ca/software/MAGeTbrain/).
The final customized atlases and surface representations of the striatum, globus pallidus, and thalamus, as well as the five MR templates can be retrieved from the Figshare repository (Data Citation 1: Figshare https://dx.doi.org/10.6084/m9.figshare.c.4052768) and is available in both MINC (http://www.bic.mni.mcgill.ca/ServicesSoftware/MINC) and NIfTI-1 (https://nifti.nimh.nih.gov/pub/dist/src/niftilib/nifti1.h) format.
Moreover, these files are also located on our GitHub repository as these atlases and surface representations are to be used with MAGeT-Brain (http://cobralab.ca/software/MAGeTbrain/). The atlas labels and surface representations for the striatum, globus palidus and thalamus can be found at https://github.com/CobraLab/atlases/tree/master/5-atlas-subcortical, while the five high-resolution MRI templates can be found here (https://github.com/CobraLab/atlases), along with our other atlases created using the same MRI templates, namely our hippocampal subfields and white matter atlases (Winterburn et al., 2011, Pipitone et al., 2014 & Amaral et al., 2016), as well as the cerebellar atlases (Park et al., 2014).
As alluded to above, the atlas resulting from the atlas-to-template warping technique delineates 108 subcortical structures however, the final atlases in which will be used as input to the MAGeT-Brain pipeline consist of only the striatum, globus pallidus and thalamus, as these structures were validated for their accuracy, using the Dice’s kappa measures. Due to limited contrast and delineation of certain structures included in the histologically-derived atlas, such as the thalamic nuclei subdivisions, the accuracy of the segmentation for all 108 structures were not validated, as we do not have a gold-standard to compare the automated labels to (due to the lack of manual segmentation protocols for these structures). Nonetheless, the full atlas is available to download, however the accuracy of the automated segmentation for all 108 structures have not been assessed and thus these labels should be used at the discretion of the reader (Data Citation 1: Figshare https://dx.doi.org/10.6084/m9.figshare.c.4052768). Moreover, neither atlas versions (the 3 structures versus the 108 structure versions) parses the striatum into the caudate nucleus, putamen and nucleus accumbens. Partial volume effects and lack of contrast between the subdivisions of the striatum (limited contrast to delineate the border between the caudate nucleus and nucleus accumbens, as well as the fine-scale cell bridges between the caudate and the putamen) were the main factors for providing a single label for the entire striatum. Any attempt to subdivide the striatum further would be based on heuristic definition.
Atlas-to-template warping technique validation
Given that manual segmentation is the current gold standard for anatomical delineation, the manual segmentations mentioned in section “Manual Segmentations” were used to evaluate the “goodness-of-fit” of the atlas labels generated using our atlas-to-template warping technique. All labels of the atlas had high overlap scores (κ>0.83 for all structures bilaterally). Average Dice’s Kappa scores across all five atlases show strong overlap between automated and manual segmentations for the striatum (mean left κ=0.885; right κ=0.882), the globus pallidus (mean left κ=0.836; right κ=0.829) and the thalamus (mean left κ=0.903; right κ=0.899). Average Dice’s Kappa scores across all atlases are shown in Table 2, and Dice’s Kappa scores for each atlas are shown in Table 3. See Figure 3 for an example of the final segmentations of striatum, globus pallidus and thalamus for one of the five atlases.
Based on the Dice’s Kappa scores obtained, the resulting segmentation of the striatum, globus pallidus and thalamus from the atlas-to-template warping is quite accurate. While all Dice’s Kappa scores obtained are accurate by segmentation literature standards, nonetheless, a possible explanation for Dice’s Kappa scores observed for the globus pallidus being lower than those in the striatum and thalamus is the limited resolution and contrast of certain of these structures in MRI data. Compared to the histologically-derived atlas, in which the resolution allows for the posterior portions of the subcortical structures to be well identified, there is limited resolution of these boundaries in MR images32. The limited contrast of the pallidal borders may be discrepant with manual segmentation protocols7,53, which attests to the challenges in its delineation, and will inevitably result in lower Dice’s Kappa overlap scores. Given the limited contrast for the pallidum in T1w images, the atlas-to-template warping technique was originally performed on high-resolution T2w MR templates however, the Dice’s kappa measures (striatum: left κ=0.803, right κ=0.817; globus pallidus: left κ=0.736, right κ=0.747; thalamus: left κ=0.811, right κ=0.809) were not nearly as high as those obtained when warping the histologically-derived atlas to T1w MR templates (Table 1). Thus, given the improved accuracy of the atlas labels produced on the T1w images, the final atlases described in the paper were derived on T1w MR templates. Another possible improvement of segmentation accuracy in the newly-created atlases could be the use of several sets of serial histological data from multiple atlases. However, multiple large section histology (as the one used as inputs here) are notoriously difficult to achieve. Thus, we chose to use the histological dataset detailed in Chakravarty et al., (2006), given that this data was well studied and delineated by a neurosurgeon from the Montreal Neurological Institute.
Nonlinear Registration Algorithms
The ANIMAL algorithm42,43 was used for the nonlinear registration of the histologically-derived atlas to the five MRI templates, which deforms a source MRI volume, the pseudo-MRI atlas, to match a target MRI volume, the MRI template. Alternate nonlinear registration algorithms were also examined. Based on the results from Klein et al.54 (2009), where 15 registration algorithms were evaluated based on overlap measures of manually labeled anatomical regions, two other of nonlinear registration algorithms were used for the nonlinear registration step of the atlas-to-template warping. Given that a symmetric normalization (SyN) nonlinear transformation delivered consistently high accuracy across subjects and label sets in Klein et al. (2009), two iterations of the SyN transformation, using two different adaptations of Advanced Normalization Tools (ANTS) registration suite55, mincANTS and antsRegistration, were implemented. However, neither implementation produced superior registrations to those of ANIMAL, as evidenced by Dice’s Kappa scores (data not shown). In keeping with the methodology for atlas-to-template warping described in Chakravarty et al. (2006), ANIMAL was used to nonlinearly register the pseudo-MRIs to the MRI templates to finalize the atlas-to-template warping. The comparison of nonlinear warping techniques for atlas-to-template MRI warping is beyond the scope of this manuscript.
Moreover, two separate ANIMAL runs were performed using different parameters; the first using the parameters described in Chakravarty et al. (2006) and a second from Chakravarty et al. (2008), where the resolution of the deformation field begins at a higher resolution in the latter paper; that is, the sublattice (the number of nodes) and the sublattice diameter (the diameter of a local spherical neighbourhood around each node) is smaller for the first step of the nonlinear algorithm (sublattice=8; sublattice diameter=10 in Chakravarty et al. (2006) whereas sublattice=6; sublattice diameter=8 in Chakravarty et al. (2008)). When using the parameters described in Chakravarty et al. (2008), the atlases generated had higher Dice’s Kappa scores, compared to the Dice’s Kappa obtained when using the Chakravarty et al. (2006) parameters. Given that the resolution of the five MRI templates (0.3 mm3 isotropic) onto which the histologically-derived atlas was warped is higher than the resolution of the Colin27 template (1.0 mm3 isotropic) used in Chakravarty et al. (2006), increasing the resolution of initial step of ANIMAL increased Dice’s Kappa scores. That is, the automated labels generated using the parameters described in Chakravarty et al. (2008) for the ANIMAL nonlinear algorithm (as described in Table 1) allows for greater accuracy in capturing the neuroanatomical variation present between the histologically-derived atlas and the MRI templates, and so, these parameters were used for the creation of the five subcortical atlases.
Recent work by Xiao et al.56,
How to cite this article: Tullo S. et al. Warping an atlas derived from serial histology to 5 high-resolution MRIs. Sci. Data 5:180107 doi: 10.1038/sdata.2018.107 (2018).
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Tullo, S. et al. Figshare https://dx.doi.org/10.6084/m9.figshare.c.4052768 (2018)