Appearance-based Segmentation
Simon Duchesne - D. Louis Collins
Montréal Neurological Institute (MNI), McGill Univ.,
Montréal, Canada H3A 2B4
{duchesne,louis}@bic.mni.mcgill.ca
Segmentation methods for brain MR images typically employ manual and/or
automatic knowledge-based models specific to the structure of interest
(SOI). The technique presented here overcomes some of the limitations of current methods. It requires no manual intervention, is fast, fully 3D, and generic yet
constrained by some form of prior structure information. The novelty of this work resides in its a priori Principal Components Analysis (PCA) of
non-linear registration data of a volume of interest
(VOI), represented by dense 3D deformation
fields from ANIMAL [1]. The results are used in an Appearance
Model, inspired by Cootes [2], able to segment any SOIs contained within the VOI, in the
atlas-independent framework described by Collins [1]. This article presents
the theoretical basis for and initial work towards hippocampus
segmentation on subject images from the MNI International Consortium
for Brain Mapping (ICBM) database.