Animal: Validation and Applications of Nonlinear Registration-Based Segmentation

Abstract

Magnetic resonance imaging (MRI) has become the modality of choice for neuro-anatomical imaging. Quantitative analysis requires the accurate and reproducible labeling of all voxels in any given structure within the brain. Since manual labeling is prohibitively time-consuming and error-prone we have designed an automated procedure called ANIMAL (Automatic Nonlinear Image Matching and Anatomical Labeling) to objectively segment gross anatomical structures from 3D MRIs of normal brains. The procedure is based on nonlinear registration with a previously labeled target brain, followed by numerical inverse transformation of the labels to the native MRI space. Besides segmentation, ANIMAL has been applied to non-rigid registration and to the analysis of morphometric variability. In this paper, the nonlinear registration approach is validated on five test volumes, produced with simulated deformations. Experiments show that the ANIMAL recovers 64% of the nonlinear residual variability remaining after linear registration. Segmentations of the same test data are presented as well. The paper concludes with two applications of ANIMAL using real data. In the first, one MRI volume is nonlinearly matched to a second and is automatically segmented using labels, predefined on the second MRI volume. The automatic segmentation compares well with manual labeling of the same structures. In the second application, ANIMAL is applied to seventeen MRI data sets, and a 3D map of anatomical variability estimates is produced. The automatic variability estimates correlate well (r =0.867, p = 0.01) with manual estimates of inter-subject variability.

Keywords

Affiliated Institutions

• McGill University CA
• Montreal Neurological Institute and Hospital CA

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