Abstract
Charting cortical growth trajectories is of paramount importance for understanding brain development. However, such analysis necessitates the collection of longitudinal data, which can be challenging due to subject dropouts and failed scans. In this paper, we will introduce a method for longitudinal prediction of cortical surfaces using a spatial graph convolutional neural network (GCNN), which extends conventional CNNs from Euclidean to curved manifolds. The proposed method is designed to model the cortical growth trajectories and jointly predict inner and outer cortical surfaces at multiple time points. Adopting a binary flag in loss calculation to deal with missing data, we fully utilize all available cortical surfaces for training our deep learning model, without requiring a complete collection of longitudinal data. Predicting the surfaces directly allows cortical attributes such as cortical thickness, curvature, and convexity to be computed for subsequent analysis. We will demonstrate with experimental results that our method is capable of capturing the nonlinearity of spatiotemporal cortical growth patterns and can predict cortical surfaces with improved accuracy.
| Original language | English |
|---|---|
| Title of host publication | Information Processing in Medical Imaging - 26th International Conference, IPMI 2019, Proceedings |
| Editors | Siqi Bao, Albert C.S. Chung, James C. Gee, Paul A. Yushkevich |
| Publisher | Springer Verlag |
| Pages | 277-288 |
| Number of pages | 12 |
| ISBN (Print) | 9783030203504 |
| DOIs | |
| Publication status | Published - 2019 Jan 1 |
| Externally published | Yes |
| Event | 26th International Conference on Information Processing in Medical Imaging, IPMI 2019 - Hong Kong, China Duration: 2019 Jun 2 → 2019 Jun 7 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 11492 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Conference
| Conference | 26th International Conference on Information Processing in Medical Imaging, IPMI 2019 |
|---|---|
| Country | China |
| City | Hong Kong |
| Period | 19/6/2 → 19/6/7 |
Fingerprint
Keywords
- Graph Convolutional Neural Networks
- Infant cortical surfaces
- Longitudinal prediction
- Missing data
- Shape Analysis
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)
Cite this
Deep Modeling of Growth Trajectories for Longitudinal Prediction of Missing Infant Cortical Surfaces. / Liu, Peirong; Wu, Zhengwang; Li, Gang; Yap, Pew Thian; Shen, Dinggang.
Information Processing in Medical Imaging - 26th International Conference, IPMI 2019, Proceedings. ed. / Siqi Bao; Albert C.S. Chung; James C. Gee; Paul A. Yushkevich. Springer Verlag, 2019. p. 277-288 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11492 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Deep Modeling of Growth Trajectories for Longitudinal Prediction of Missing Infant Cortical Surfaces
AU - Liu, Peirong
AU - Wu, Zhengwang
AU - Li, Gang
AU - Yap, Pew Thian
AU - Shen, Dinggang
PY - 2019/1/1
Y1 - 2019/1/1
N2 - Charting cortical growth trajectories is of paramount importance for understanding brain development. However, such analysis necessitates the collection of longitudinal data, which can be challenging due to subject dropouts and failed scans. In this paper, we will introduce a method for longitudinal prediction of cortical surfaces using a spatial graph convolutional neural network (GCNN), which extends conventional CNNs from Euclidean to curved manifolds. The proposed method is designed to model the cortical growth trajectories and jointly predict inner and outer cortical surfaces at multiple time points. Adopting a binary flag in loss calculation to deal with missing data, we fully utilize all available cortical surfaces for training our deep learning model, without requiring a complete collection of longitudinal data. Predicting the surfaces directly allows cortical attributes such as cortical thickness, curvature, and convexity to be computed for subsequent analysis. We will demonstrate with experimental results that our method is capable of capturing the nonlinearity of spatiotemporal cortical growth patterns and can predict cortical surfaces with improved accuracy.
AB - Charting cortical growth trajectories is of paramount importance for understanding brain development. However, such analysis necessitates the collection of longitudinal data, which can be challenging due to subject dropouts and failed scans. In this paper, we will introduce a method for longitudinal prediction of cortical surfaces using a spatial graph convolutional neural network (GCNN), which extends conventional CNNs from Euclidean to curved manifolds. The proposed method is designed to model the cortical growth trajectories and jointly predict inner and outer cortical surfaces at multiple time points. Adopting a binary flag in loss calculation to deal with missing data, we fully utilize all available cortical surfaces for training our deep learning model, without requiring a complete collection of longitudinal data. Predicting the surfaces directly allows cortical attributes such as cortical thickness, curvature, and convexity to be computed for subsequent analysis. We will demonstrate with experimental results that our method is capable of capturing the nonlinearity of spatiotemporal cortical growth patterns and can predict cortical surfaces with improved accuracy.
KW - Graph Convolutional Neural Networks
KW - Infant cortical surfaces
KW - Longitudinal prediction
KW - Missing data
KW - Shape Analysis
UR - http://www.scopus.com/inward/record.url?scp=85066148992&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85066148992&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-20351-1_21
DO - 10.1007/978-3-030-20351-1_21
M3 - Conference contribution
AN - SCOPUS:85066148992
SN - 9783030203504
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 277
EP - 288
BT - Information Processing in Medical Imaging - 26th International Conference, IPMI 2019, Proceedings
A2 - Bao, Siqi
A2 - Chung, Albert C.S.
A2 - Gee, James C.
A2 - Yushkevich, Paul A.
PB - Springer Verlag
ER -