Deep Modeling of Growth Trajectories for Longitudinal Prediction of Missing Infant Cortical Surfaces

Peirong Liu, Zhengwang Wu, Gang Li, Pew Thian Yap, Dinggang Shen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish
Title of host publicationInformation Processing in Medical Imaging - 26th International Conference, IPMI 2019, Proceedings
EditorsSiqi Bao, Albert C.S. Chung, James C. Gee, Paul A. Yushkevich
PublisherSpringer Verlag
Pages277-288
Number of pages12
ISBN (Print)9783030203504
DOIs
Publication statusPublished - 2019 Jan 1
Externally publishedYes
Event26th International Conference on Information Processing in Medical Imaging, IPMI 2019 - Hong Kong, China
Duration: 2019 Jun 22019 Jun 7

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11492 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference26th International Conference on Information Processing in Medical Imaging, IPMI 2019
CountryChina
CityHong Kong
Period19/6/219/6/7

Fingerprint

Trajectories
Trajectory
Prediction
Modeling
Longitudinal Data
Spatial Graph
Predict
Drop out
Missing Data
Convexity
Euclidean
Brain
Curvature
Attribute
Nonlinearity
Neural Networks
Binary
Neural networks
Experimental Results
Model

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

Liu, P., Wu, Z., Li, G., Yap, P. T., & Shen, D. (2019). Deep Modeling of Growth Trajectories for Longitudinal Prediction of Missing Infant Cortical Surfaces. In S. Bao, A. C. S. Chung, J. C. Gee, & P. A. Yushkevich (Eds.), Information Processing in Medical Imaging - 26th International Conference, IPMI 2019, Proceedings (pp. 277-288). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11492 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-20351-1_21

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 proceedingConference contribution

Liu, P, Wu, Z, Li, G, Yap, PT & Shen, D 2019, Deep Modeling of Growth Trajectories for Longitudinal Prediction of Missing Infant Cortical Surfaces. in S Bao, ACS Chung, JC Gee & PA Yushkevich (eds), Information Processing in Medical Imaging - 26th International Conference, IPMI 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11492 LNCS, Springer Verlag, pp. 277-288, 26th International Conference on Information Processing in Medical Imaging, IPMI 2019, Hong Kong, China, 19/6/2. https://doi.org/10.1007/978-3-030-20351-1_21
Liu P, Wu Z, Li G, Yap PT, Shen D. Deep Modeling of Growth Trajectories for Longitudinal Prediction of Missing Infant Cortical Surfaces. In Bao S, Chung ACS, Gee JC, Yushkevich PA, editors, Information Processing in Medical Imaging - 26th International Conference, IPMI 2019, Proceedings. Springer Verlag. 2019. p. 277-288. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-030-20351-1_21
Liu, Peirong ; Wu, Zhengwang ; Li, Gang ; Yap, Pew Thian ; Shen, Dinggang. / Deep Modeling of Growth Trajectories for Longitudinal Prediction of Missing Infant Cortical Surfaces. Information Processing in Medical Imaging - 26th International Conference, IPMI 2019, Proceedings. editor / Siqi Bao ; Albert C.S. Chung ; James C. Gee ; Paul A. Yushkevich. Springer Verlag, 2019. pp. 277-288 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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