Appearance-based segmentation of medial temporal lobe structures.
A new paradigm for the characterization of structure appearance is proposed, based on a combination of gray-level MRI intensity data and a shape descriptor derived from a priori principal components analysis of 3D deformation vector fields. Generated without external intervention, it extends into 3D more classic, 2D manual landmark-based shape models. Application of this novel concept led to a method for the segmentation of medial temporal lobe structures from brain magnetic resonance images. The strategy employed for segmentation aims at synthesizing, using the appearance model, a deformation field that maps a new volume onto a reference target. Any information defined on the reference can then be propagated back on the new volume instance, thereby achieving segmentation. The proposed method was tested on a data set of 80 normal subjects and compared against manual segmentation as well as automated segmentation results from ANIMAL, a nonlinear registration and segmentation technique. Experimental results demonstrated the robustness and flexibility of the new method. Segmentation accuracy, measured by overlap statistics, is marginally lower (< 2%) than ANIMAL, while processing time is six times faster. Finally, the applicability of this concept toward shape deformation analysis is presented.
Duchesne
S
S
McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, 3801 University Street, Montreal, Canada. duchesne@bic.mni.mcgill.ca
Pruessner
J
J
Collins
D L
DL
D000465:Algorithms; D000818:Animals; D001931:Brain Mapping / Q000295:instrumentation / Q000379:methods; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008959:Models, Neurological; D015203:Reproducibility of Results; D012984:Software; D013702:Temporal Lobe / Q000033:anatomy & histology*