ANIMAL+INSECT

Improved automatic gross cerebral structure segmentation $^\dagger$

DL Collins, AP Zijdenbos and AC Evans

McConnell Brain Imaging Centre, Montréal Neurological Institute,
McGill University, Montréal, Canada

Abstract:

The non-invasive in-vivo nature of magnetic resonance imaging (MRI) makes it the modality of choice for many neuro-anatomical imaging studies. The current trend from qualitative to quantitative analysis often requires identification of specific anatomical regions. In this poster, an algorithm is presented that merges the output of a tissue classification process with automatic region identification to accurately segment gross anatomical structures within the human brain.

Introduction

Quantitative analysis of neuro-anatomical or neuro-functional data often requires explicit regional identification of gross anatomical structures. Unfortunately, manual segmentation is time-consuming, subjective and error prone. Furthermore, inter- and intra-observer variability may reduce detectability of subtle differences when making comparisons. To address this problem, we have developed two complementary segmentation strategies for automatic identification of tissue types and gross anatomical structures from volumetric magnetic resonance images (MRI) of the human brain.

The first, named

(Automatic Nonlinear Image Matching and Anatomical Labeling, [1]), was designed to segment anatomical structures by computing a non-linear warp to register a given subject's MRI to a pre-labeled target MRI that serves as an atlas. The inverse of the recovered transformation is used to warp the atlas labels back onto the subject's MRI, thus achieving segmentation.

has been shown to successfully segment basal ganglia structures [1] but it has not been able to segment cortical structures satisfactorily (voxel-based overlap indexes have been typically around 40%). This is due to the fact that the deformation field estimated by

does not have the power to unfold the cortex of one brain and then refold it back onto a target brain. The deformation field is bandlimited and therefore does not have high enough frequencies to introduce (or remove) cortical folds where needed.

The second segmentation strategy is named

(Intensity Normalized Stereotaxic Environment for the Classification of Tissue). Complementary to

was designed to automatically identify different tissue types from either single channel or multi-spectral MRI data. This method has been shown to successfully classify grey matter (GM), white matter (WM), and cerebral spinal fluid (CSF) and has been applied to the problem of automatically segmenting multiple sclerosis (MS) lesions from a large number of subjects in the context of a third phase clinical trial [8]. While classification techniques such as

are able to separate GM, WM and CSF and in so doing, extract fine detail from the MRI volume, they cannot differentiate between two adjacent structures with the same tissue type.

By merging the complementary information from

's non-linear deformation with the output of

's classification technique, it is possible to accurately identify specific cortical structures from a subject's MRI.

Methods

ANIMAL

The

algorithm deforms one MRI volume to match another, previously labelled, MRI volume. It builds up the 3D non-linear deformation field in a local fashion, fitting spherical neighbourhoods in sequence. Each local neighbourhood in one volume is translated to achieve an optimal match within the other volume. Neighbourhoods are arranged in a 3D grid to fill the volume and each one moves within a range defined by the grid-spacing. The algorithm is applied in a multi-scale hierarchy. At each step, the image volumes are pre-convolved with a 3D Gaussian kernel where blurring and neighbourhood size are reduced at after each stage. Initial fits are obtained rapidly since at lower scales, only gross distortions are considered, but later iterations at finer scales accommodate local differences at the price of increasing computational burden. Segmentation is achieved by transforming labels from the second volume onto the first volume, via the spatial mapping of the 3D deformation field.

The main advantage of this type of segmentation methodology is that it results in atlas-independent segmentation since structure identification is simply a by-product of non-linear registration. The anatomical labels defined on the target volume are not used in any way to determine the match between data and model. Therefore, any atlas defined on the target MRI brain volume can be used for segmentation, thereby allowing for the co-existence of multiple atlases, each of which is simultaneously mappable to the native MR image volume without the CPU-expensive recalculation of the non-linear spatial transformation required for registration.

INSECT

is a production pipeline aimed at the fully automatic analysis of large volumes of MRI data [7]. It is based on stereotaxic registration which maps the MRI data into a standardized, Talairach coordinate space using a 9-parameter linear transformation [3] that allows, among others, the use of spatial masks and priors, facilitates longitudinal and between-group analysis, and provides a means for voxel-based statistical analysis.

assigns each voxel in stereotaxic space a tissue class label (e.g., WM, GM, CSF) using an artificial neural network (ANN) classifier. The classifier relies on input from one or more MRI volumes (e.g., T₁-, T₂-, and PD-weighted scans), which have previously been registered with and resampled into stereotaxic space. The ANN is trained for each individual subject scan using a fixed set of stereotaxic training locations for each tissue class, derived from SPAMs of WM, GM, and CSF [5].

Merging ANIMAL and INSECT

The complementary information from

's non-linear deformation (i.e., low resolution region identification) was merged with the output of

's classification technique (i.e., voxel class labels) in order to accurately identify specific structures in a subjects MRI (see Fig. 1). In short, the grey matter (extracted by

) is masked by a structural region mask (defined by

) in order to define specific cortical brain structures. Similarly, CSF-labeled voxels are masked with ventricular masks to identify the lateral, third and forth ventricles.

Results

Figure 2 shows a comparison of an

-only segmentation, an

segmentation and a manual segmentation. Not only is the

segmentation improved at the cortex, where some grey-matter regions were missed with the standard

technique, the segmentation of the lateral ventricles is much better as well (on other planes). Where the

technique overestimated the size of the ventricle, the

is in complete agreement with the MRI anatomy and with the expert's labelling.


input MRI		-only	+	manual
	classification	segmentation	segmentation	segmentation

Result of

segmentation.

Applications

The

method has been applied to 150 data sets acquired as part of the HBMP-funded International Consortium for Brain Mapping (ICBM) project [6] where the goal is to estimate structure volumes [2] and to create a SPAM (statistical probability anatomy map) atlas [4] that quantifies the normal anatomical variability of both shape and position for the different regions of the normal brain.

Discussion and Conclusion

Since

yields high resolution structure information, it is no longer necessary to run

to fine resolutions, thus providing a considerable improvement in speed. While the procedure described here uses two algorithms that were developed at the Montreal Neurological Institute, the new improved segmentation method is not dependent on these particular methods. In fact, any classification method that differentiates tissue types and any non-linear registration method may be merged to maximize the complementary information of both techniques. The method presented is completely automatic, therefore fully objective and applicable to large ensembles of brain volumes.

$^\dagger$ Acknowledgments

The authors would like to express their appreciation for support from the Human Frontier Science Project Organization, the Canadian Medical Research Council (SP-30), the McDonnell-Pew Cognitive Neuroscience Center Program, the U.S. Human Brain Map Project (HBMP), NIMH and NIDA. This work forms part of a continuing project of the HBMP-funded International Consortium for Brain Mapping (ICBM) to develop a probabilistic atlas of human neuroanatomy. We also wish to acknowledge the manual anatomical labelling completed by Colin Holmes and Noor Kabani.