TY - GEN
T1 - Segmentation of brain images using adaptive atlases with application to ventriculomegaly
AU - Shiee, Navid
AU - Bazin, Pierre Louis
AU - Cuzzocreo, Jennifer L.
AU - Blitz, Ari
AU - Pham, Dzung L.
PY - 2011
Y1 - 2011
N2 - Segmentation of brain images often requires a statistical atlas for providing prior information about the spatial position of different structures. A major limitation of atlas-based segmentation algorithms is their deficiency in analyzing brains that have a large deviation from the population used in the construction of the atlas. We present an expectation-maximization framework based on a Dirichlet distribution to adapt a statistical atlas to the underlying subject. Our model combines anatomical priors with the subject's own anatomy, resulting in a subject specific atlas which we call an "adaptive atlas". The generation of this adaptive atlas does not require the subject to have an anatomy similar to that of the atlas population, nor does it rely on the availability of an ensemble of similar images. The proposed method shows a significant improvement over current segmentation approaches when applied to subjects with severe ventriculomegaly, where the anatomy deviates significantly from the atlas population. Furthermore, high levels of accuracy are maintained when the method is applied to subjects with healthy anatomy.
AB - Segmentation of brain images often requires a statistical atlas for providing prior information about the spatial position of different structures. A major limitation of atlas-based segmentation algorithms is their deficiency in analyzing brains that have a large deviation from the population used in the construction of the atlas. We present an expectation-maximization framework based on a Dirichlet distribution to adapt a statistical atlas to the underlying subject. Our model combines anatomical priors with the subject's own anatomy, resulting in a subject specific atlas which we call an "adaptive atlas". The generation of this adaptive atlas does not require the subject to have an anatomy similar to that of the atlas population, nor does it rely on the availability of an ensemble of similar images. The proposed method shows a significant improvement over current segmentation approaches when applied to subjects with severe ventriculomegaly, where the anatomy deviates significantly from the atlas population. Furthermore, high levels of accuracy are maintained when the method is applied to subjects with healthy anatomy.
UR - http://www.scopus.com/inward/record.url?scp=79959578104&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79959578104&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-22092-0_1
DO - 10.1007/978-3-642-22092-0_1
M3 - Conference contribution
AN - SCOPUS:79959578104
SN - 9783642220913
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 1
EP - 12
BT - Information Processing in Medical Imaging - 22nd International Conference, IPMI 2011, Proceedings
T2 - 22nd International Conference on Information Processing in Medical Imaging, IPMI 2011
Y2 - 3 July 2011 through 8 July 2011
ER -