@inproceedings{45242b0f3972416e9c2bddcfe257e849,
title = "Deep Modeling of Growth Trajectories for Longitudinal Prediction of Missing Infant Cortical Surfaces",
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.",
keywords = "Graph Convolutional Neural Networks, Infant cortical surfaces, Longitudinal prediction, Missing data, Shape Analysis",
author = "Peirong Liu and Zhengwang Wu and Gang Li and Yap, {Pew Thian} and Dinggang Shen",
year = "2019",
doi = "10.1007/978-3-030-20351-1_21",
language = "English",
isbn = "9783030203504",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "277--288",
editor = "Chung, {Albert C.S.} and Siqi Bao and Gee, {James C.} and Yushkevich, {Paul A.}",
booktitle = "Information Processing in Medical Imaging - 26th International Conference, IPMI 2019, Proceedings",
note = "26th International Conference on Information Processing in Medical Imaging, IPMI 2019 ; Conference date: 02-06-2019 Through 07-06-2019",
}