Abstract
Magnetic resonance imaging (MRI) of pediatric brain provides invaluable information for early normal and abnormal brain development. Longitudinal neuroimaging has spanned various research works on examining infant brain development patterns. However, studies on predicting postnatal brain image evolution remain scarce, which is very challenging due to the dynamic tissue contrast change and even inversion in postnatal brains. In this paper, we unprecedentedly propose a dual image intensity and anatomical structure (label) prediction framework that nicely links the geodesic image metamorphosis model with sparse patch-based image representation, thereby defining spatiotemporal metamorphic patches encoding both image photometric and geometric deformation. In the training stage, we learn the 4D metamorphosis trajectories for each training subject. In the prediction stage, we define various strategies to sparsely represent each patch in the testing image using the training metamorphosis patches; while progressively incrementing the richness of the patch (from appearance-based to multimodal kinetic patches). We used the proposed framework to predict 6, 9 and 12-month brain MR image intensity and structure (white and gray matter maps) from 3 months in 10 infants. The proposed framework showed promising preliminary prediction results for the spatiotemporally complex, drastically changing brain images.
| Original language | English |
|---|---|
| Title of host publication | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Publisher | Springer Verlag |
| Pages | 197-204 |
| Number of pages | 8 |
| Volume | 9467 |
| ISBN (Print) | 9783319281933 |
| DOIs | |
| Publication status | Published - 2015 |
| Externally published | Yes |
| Event | 1st International Workshop on Patch-Based Techniques in Medical Imaging, Patch-MI 2015 - Munich, Germany Duration: 2015 Oct 9 → 2015 Oct 9 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 9467 |
| ISSN (Print) | 03029743 |
| ISSN (Electronic) | 16113349 |
Other
| Other | 1st International Workshop on Patch-Based Techniques in Medical Imaging, Patch-MI 2015 |
|---|---|
| Country | Germany |
| City | Munich |
| Period | 15/10/9 → 15/10/9 |
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ASJC Scopus subject areas
- Computer Science(all)
- Theoretical Computer Science
Cite this
Prediction of infant MRI appearance and anatomical structure evolution using sparse patch-based metamorphosis learning framework. / Rekik, Islem; Li, Gang; Wu, Guorong; Lin, Weili; Shen, Dinggang.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9467 Springer Verlag, 2015. p. 197-204 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9467).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Prediction of infant MRI appearance and anatomical structure evolution using sparse patch-based metamorphosis learning framework
AU - Rekik, Islem
AU - Li, Gang
AU - Wu, Guorong
AU - Lin, Weili
AU - Shen, Dinggang
PY - 2015
Y1 - 2015
N2 - Magnetic resonance imaging (MRI) of pediatric brain provides invaluable information for early normal and abnormal brain development. Longitudinal neuroimaging has spanned various research works on examining infant brain development patterns. However, studies on predicting postnatal brain image evolution remain scarce, which is very challenging due to the dynamic tissue contrast change and even inversion in postnatal brains. In this paper, we unprecedentedly propose a dual image intensity and anatomical structure (label) prediction framework that nicely links the geodesic image metamorphosis model with sparse patch-based image representation, thereby defining spatiotemporal metamorphic patches encoding both image photometric and geometric deformation. In the training stage, we learn the 4D metamorphosis trajectories for each training subject. In the prediction stage, we define various strategies to sparsely represent each patch in the testing image using the training metamorphosis patches; while progressively incrementing the richness of the patch (from appearance-based to multimodal kinetic patches). We used the proposed framework to predict 6, 9 and 12-month brain MR image intensity and structure (white and gray matter maps) from 3 months in 10 infants. The proposed framework showed promising preliminary prediction results for the spatiotemporally complex, drastically changing brain images.
AB - Magnetic resonance imaging (MRI) of pediatric brain provides invaluable information for early normal and abnormal brain development. Longitudinal neuroimaging has spanned various research works on examining infant brain development patterns. However, studies on predicting postnatal brain image evolution remain scarce, which is very challenging due to the dynamic tissue contrast change and even inversion in postnatal brains. In this paper, we unprecedentedly propose a dual image intensity and anatomical structure (label) prediction framework that nicely links the geodesic image metamorphosis model with sparse patch-based image representation, thereby defining spatiotemporal metamorphic patches encoding both image photometric and geometric deformation. In the training stage, we learn the 4D metamorphosis trajectories for each training subject. In the prediction stage, we define various strategies to sparsely represent each patch in the testing image using the training metamorphosis patches; while progressively incrementing the richness of the patch (from appearance-based to multimodal kinetic patches). We used the proposed framework to predict 6, 9 and 12-month brain MR image intensity and structure (white and gray matter maps) from 3 months in 10 infants. The proposed framework showed promising preliminary prediction results for the spatiotemporally complex, drastically changing brain images.
UR - http://www.scopus.com/inward/record.url?scp=84955249351&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84955249351&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-28194-0_24
DO - 10.1007/978-3-319-28194-0_24
M3 - Conference contribution
AN - SCOPUS:84955249351
SN - 9783319281933
VL - 9467
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 197
EP - 204
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PB - Springer Verlag
ER -