Abstract
Manifold is often used to characterize the high-dimensional distribution of individual brain MR images. The deformation field, used to register the subject with the template, is perceived as the geodesic pathway between images on the manifold. Generally, it is non-trivial to estimate the deformation pathway directly due to the intrinsic complexity of the manifold. In this work, we break the restriction of the single and complex manifold, by short-circuiting the subject-template pathway with routes from multiple simpler manifolds. Specifically, we reduce the anatomical complexity of the subject/template images, and project them to the virtual and simplified manifolds. The projected simple images then guide the subject image to complete its journey toward the template image space step by step. In the final, the subject-template pathway is computed by traversing multiple manifolds of lower complexity, rather than depending on the original single complex manifold only. We validate the cross-manifold guidance and apply it to brain MR image registration. We conclude that our method leads to superior alignment accuracy compared to state-of-the-art deformable registration techniques.
| Original language | English |
|---|---|
| Title of host publication | Medical Imaging and Augmented Reality - 7th International Conference, MIAR 2016, Proceedings |
| Publisher | Springer Verlag |
| Pages | 415-424 |
| Number of pages | 10 |
| Volume | 9805 |
| ISBN (Print) | 9783319437743 |
| DOIs | |
| Publication status | Published - 2016 |
| Externally published | Yes |
| Event | 7th International Conference on Medical Imaging and Augmented Reality, MIAR 2016 - Bern, Switzerland Duration: 2016 Aug 24 → 2016 Aug 26 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 9805 |
| ISSN (Print) | 03029743 |
| ISSN (Electronic) | 16113349 |
Other
| Other | 7th International Conference on Medical Imaging and Augmented Reality, MIAR 2016 |
|---|---|
| Country | Switzerland |
| City | Bern |
| Period | 16/8/24 → 16/8/26 |
Fingerprint
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)
Cite this
Cross-manifold guidance in deformable registration of brain MR images. / Zhang, Jinpeng; Wang, Qian; Wu, Guorong; Shen, Dinggang.
Medical Imaging and Augmented Reality - 7th International Conference, MIAR 2016, Proceedings. Vol. 9805 Springer Verlag, 2016. p. 415-424 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9805).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Cross-manifold guidance in deformable registration of brain MR images
AU - Zhang, Jinpeng
AU - Wang, Qian
AU - Wu, Guorong
AU - Shen, Dinggang
PY - 2016
Y1 - 2016
N2 - Manifold is often used to characterize the high-dimensional distribution of individual brain MR images. The deformation field, used to register the subject with the template, is perceived as the geodesic pathway between images on the manifold. Generally, it is non-trivial to estimate the deformation pathway directly due to the intrinsic complexity of the manifold. In this work, we break the restriction of the single and complex manifold, by short-circuiting the subject-template pathway with routes from multiple simpler manifolds. Specifically, we reduce the anatomical complexity of the subject/template images, and project them to the virtual and simplified manifolds. The projected simple images then guide the subject image to complete its journey toward the template image space step by step. In the final, the subject-template pathway is computed by traversing multiple manifolds of lower complexity, rather than depending on the original single complex manifold only. We validate the cross-manifold guidance and apply it to brain MR image registration. We conclude that our method leads to superior alignment accuracy compared to state-of-the-art deformable registration techniques.
AB - Manifold is often used to characterize the high-dimensional distribution of individual brain MR images. The deformation field, used to register the subject with the template, is perceived as the geodesic pathway between images on the manifold. Generally, it is non-trivial to estimate the deformation pathway directly due to the intrinsic complexity of the manifold. In this work, we break the restriction of the single and complex manifold, by short-circuiting the subject-template pathway with routes from multiple simpler manifolds. Specifically, we reduce the anatomical complexity of the subject/template images, and project them to the virtual and simplified manifolds. The projected simple images then guide the subject image to complete its journey toward the template image space step by step. In the final, the subject-template pathway is computed by traversing multiple manifolds of lower complexity, rather than depending on the original single complex manifold only. We validate the cross-manifold guidance and apply it to brain MR image registration. We conclude that our method leads to superior alignment accuracy compared to state-of-the-art deformable registration techniques.
UR - http://www.scopus.com/inward/record.url?scp=84984846211&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84984846211&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-43775-0_38
DO - 10.1007/978-3-319-43775-0_38
M3 - Conference contribution
AN - SCOPUS:84984846211
SN - 9783319437743
VL - 9805
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 415
EP - 424
BT - Medical Imaging and Augmented Reality - 7th International Conference, MIAR 2016, Proceedings
PB - Springer Verlag
ER -