Unbiased diffeomorphic atlas construction for computational anatomy

• Published in: NeuroImage (Orlando, Fla.) Vol. 23; pp. S151 - S160
• Main Authors: Joshi, S., Davis, Brad, Jomier, Matthieu, Gerig, Guido
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 2004; Elsevier Limited
• Subjects: Algorithms; Atlases as Topic; Autistic Disorder - pathology; Bias; Brain - anatomy & histology; Brain - pathology; Brain atlases; Brain Mapping - methods; Brain research; Caudate Nucleus - anatomy & histology; Caudate Nucleus - physiology; Child; Computational anatomy; Construction; Databases, Factual; Deformation; Geometry; Hilbert space; Humans; Image Processing, Computer-Assisted; Image segmentation; Magnetic Resonance Imaging - statistics & numerical data; Mathematical functions; Medical research; Models, Statistical; Population; Registration; Reproducibility of Results; Statistical methods; Image segmentation; Brain atlases; Computational anatomy; Registration
• ISSN: 1053-8119; 1095-9572
• DOI: 10.1016/j.neuroimage.2004.07.068

Construction of population atlases is a key issue in medical image analysis, and particularly in brain mapping. Large sets of images are mapped into a common coordinate system to study intra-population variability and inter-population differences, to provide voxel-wise mapping of functional sites, and help tissue and object segmentation via registration of anatomical labels. Common techniques often include the choice of a template image, which inherently introduces a bias. This paper describes a new method for unbiased construction of atlases in the large deformation diffeomorphic setting. A child neuroimaging autism study serves as a driving application. There is lack of normative data that explains average brain shape and variability at this early stage of development. We present work in progress toward constructing an unbiased MRI atlas of 2 years of children and the building of a probabilistic atlas of anatomical structures, here the caudate nucleus. Further, we demonstrate the segmentation of new subjects via atlas mapping. Validation of the methodology is performed by comparing the deformed probabilistic atlas with existing manual segmentations.