Automatic construction of 3-D statistical deformation models of the brain using nonrigid registration

• Published in: IEEE transactions on medical imaging Vol. 22; no. 8; pp. 1014 - 1025
• Main Authors: Rueckert, D., Frangi, A.F., Schnabel, J.A.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01-08-2003; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Anatomy; Anatomy, Cross-Sectional - methods; Biological and medical sciences; Brain - pathology; Brain modeling; Construction; Deformable models; Deformation; Image Interpretation, Computer-Assisted - methods; Image segmentation; Imaging, Three-Dimensional - methods; Information analysis; Magnetic resonance; Magnetic Resonance Imaging - methods; Medical sciences; Models, Biological; Models, Statistical; Mutual information; Reproducibility of Results; Schizophrenia - diagnosis; Sensitivity and Specificity; Shape; Statistical analysis; Subtraction Technique; Tomography; Tomography, X-Ray Computed - methods
• ISSN: 0278-0062; 1558-254X
• DOI: 10.1109/TMI.2003.815865

In this paper, we show how the concept of statistical deformation models (SDMs) can be used for the construction of average models of the anatomy and their variability. SDMs are built by performing a statistical analysis of the deformations required to map anatomical features in one subject into the corresponding features in another subject. The concept of SDMs is similar to statistical shape models (SSMs) which capture statistical information about shapes across a population, but offers several advantages over SSMs. First, SDMs can be constructed directly from images such as three-dimensional (3-D) magnetic resonance (MR) or computer tomography volumes without the need for segmentation which is usually a prerequisite for the construction of SSMs. Instead, a nonrigid registration algorithm based on free-form deformations and normalized mutual information is used to compute the deformations required to establish dense correspondences between the reference subject and the subjects in the population class under investigation. Second, SDMs allow the construction of an atlas of the average anatomy as well as its variability across a population of subjects. Finally, SDMs take the 3-D nature of the underlying anatomy into account by analysing dense 3-D deformation fields rather than only information about the surface shape of anatomical structures. We show results for the construction of anatomical models of the brain from the MR images of 25 different subjects. The correspondences obtained by the nonrigid registration are evaluated using anatomical landmark locations and show an average error of 1.40 mm at these anatomical landmark positions. We also demonstrate that SDMs can be constructed so as to minimize the bias toward the chosen reference subject.