Abstract
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.
| Original language | English |
|---|---|
| Title of host publication | Patch-Based Techniques in Medical Imaging - 2nd International Workshop, Patch-MI 2016 held in conjunction with MICCAI 2016, Proceedings |
| Publisher | Springer Verlag |
| Pages | 34-42 |
| Number of pages | 9 |
| Volume | 9993 LNCS |
| ISBN (Print) | 9783319471174 |
| DOIs | |
| Publication status | Published - 2016 |
| Externally published | Yes |
| Event | 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 - Athens, Greece Duration: 2016 Oct 17 → 2016 Oct 17 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 9993 LNCS |
| ISSN (Print) | 03029743 |
| ISSN (Electronic) | 16113349 |
Other
| Other | 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 |
|---|---|
| Country | Greece |
| City | Athens |
| Period | 16/10/17 → 16/10/17 |
Fingerprint
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)
Cite this
Consistent multi-atlas hippocampus segmentation for longitudinal MR brain images with temporal sparse representation. / Wang, Lin; Guo, Yanrong; Cao, Xiaohuan; Wu, Guorong; Shen, Dinggang.
Patch-Based Techniques in Medical Imaging - 2nd International Workshop, Patch-MI 2016 held in conjunction with MICCAI 2016, Proceedings. Vol. 9993 LNCS Springer Verlag, 2016. p. 34-42 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9993 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
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
VL - 9993 LNCS
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
PB - Springer Verlag
ER -