TY - GEN
T1 - Consistent multi-atlas hippocampus segmentation for longitudinal MR brain images with temporal sparse representation
AU - Wang, Lin
AU - Guo, Yanrong
AU - Cao, Xiaohuan
AU - Wu, Guorong
AU - Shen, Dinggang
PY - 2016
Y1 - 2016
N2 - In this paper, we propose a novel multi-atlas based longitudinal label fusion method with temporal sparse representation technique to segment hippocampi at all time points simultaneously. First, we use groupwise longitudinal registration to simultaneously (1) estimate a group-mean image of a subject image sequence and (2) register its all time-point images to the estimated groupmean image consistently over time. Then, by registering all atlases with the group-mean image, we can align all atlases longitudinally consistently to each time point of the subject image sequence. Finally, we propose a longitudinal label fusion method to propagate all atlas labels to the subject image sequence by simultaneously labeling a set of temporally-corresponded voxels with a temporal consistency constraint on sparse representation. Experimental results demonstrate that our proposed method can achieve more accurate and consistent hippocampus segmentation than the state-of-the-art counterpart methods.
AB - In this paper, we propose a novel multi-atlas based longitudinal label fusion method with temporal sparse representation technique to segment hippocampi at all time points simultaneously. First, we use groupwise longitudinal registration to simultaneously (1) estimate a group-mean image of a subject image sequence and (2) register its all time-point images to the estimated groupmean image consistently over time. Then, by registering all atlases with the group-mean image, we can align all atlases longitudinally consistently to each time point of the subject image sequence. Finally, we propose a longitudinal label fusion method to propagate all atlas labels to the subject image sequence by simultaneously labeling a set of temporally-corresponded voxels with a temporal consistency constraint on sparse representation. Experimental results demonstrate that our proposed method can achieve more accurate and consistent hippocampus segmentation than the state-of-the-art counterpart methods.
UR - http://www.scopus.com/inward/record.url?scp=84992504856&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84992504856&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-47118-1_5
DO - 10.1007/978-3-319-47118-1_5
M3 - Conference contribution
C2 - 30294728
AN - SCOPUS:84992504856
SN - 9783319471174
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 34
EP - 42
BT - Patch-Based Techniques in Medical Imaging - 2nd International Workshop, Patch-MI 2016 held in conjunction with MICCAI 2016, Proceedings
A2 - Coupe, Pierrick
A2 - Munsell, Brent C.
A2 - Rueckert, Daniel
A2 - Zhan, Yiqiang
A2 - Wu, Guorong
PB - Springer Verlag
T2 - 2nd International Workshop on Patch-Based Techniques in Medical Imaging, Patch-MI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2016
Y2 - 17 October 2016 through 17 October 2016
ER -