TY - GEN
T1 - Multi-atlas spectral PatchMatch
T2 - 17th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014
AU - Shi, Wenzhe
AU - Lombaert, Herve
AU - Bai, Wenjia
AU - Ledig, Christian
AU - Zhuang, Xiahai
AU - Marvao, Antonio
AU - Dawes, Timothy
AU - O'Regan, Declan
AU - Rueckert, Daniel
PY - 2014
Y1 - 2014
N2 - The automatic segmentation of cardiac magnetic resonance images poses many challenges arising from the large variation between different anatomies, scanners and acquisition protocols. In this paper, we address these challenges with a global graph search method and a novel spectral embedding of the images. Firstly, we propose the use of an approximate graph search approach to initialize patch correspondences between the image to be segmented and a database of labelled atlases. Then, we propose an innovative spectral embedding using a multi-layered graph of the images in order to capture global shape properties. Finally, we estimate the patch correspondences based on a joint spectral representation of the image and atlases. We evaluated the proposed approach using 155 images from the recent MICCAI SATA segmentation challenge and demonstrated that the proposed algorithm significantly outperforms current state-of-the-art methods on both training and test sets.
AB - The automatic segmentation of cardiac magnetic resonance images poses many challenges arising from the large variation between different anatomies, scanners and acquisition protocols. In this paper, we address these challenges with a global graph search method and a novel spectral embedding of the images. Firstly, we propose the use of an approximate graph search approach to initialize patch correspondences between the image to be segmented and a database of labelled atlases. Then, we propose an innovative spectral embedding using a multi-layered graph of the images in order to capture global shape properties. Finally, we estimate the patch correspondences based on a joint spectral representation of the image and atlases. We evaluated the proposed approach using 155 images from the recent MICCAI SATA segmentation challenge and demonstrated that the proposed algorithm significantly outperforms current state-of-the-art methods on both training and test sets.
UR - http://www.scopus.com/inward/record.url?scp=84909589093&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-10404-1_44
DO - 10.1007/978-3-319-10404-1_44
M3 - Conference contribution
C2 - 25333137
AN - SCOPUS:84909589093
SN - 9783319104034
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 348
EP - 355
BT - Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014 - 17th International Conference, Proceedings
PB - Springer Verlag
Y2 - 14 September 2014 through 18 September 2014
ER -