@inproceedings{39b9f33c7ed14c1cae5cc0ad90d02cdb,
title = "Cranial Meninges Reconstruction Based on Convolutional Networks and Deformable Models: Applications to Longitudinal Study of Normal Aging",
abstract = "The cranial meninges are membranes enveloping the brain. The space between these membranes contains mainly cerebrospinal fluid. It is of interest to study how the volumes of this space change with respect to normal aging. In this work, we propose to combine convolutional neural networks (CNNs) with nested topology-preserving geometric deformable models (NTGDMs) to reconstruct meningeal surfaces from magnetic resonance (MR) images. We first use CNNs to predict implicit representations of these surfaces then refine them with NTGDMs to achieve sub-voxel accuracy while maintaining spherical topology and the correct anatomical ordering. MR contrast harmonization is used to match the contrasts between training and testing images. We applied our algorithm to a subset of healthy subjects from the Baltimore Longitudinal Study of Aging for demonstration purposes and conducted longitudinal statistical analysis of the intracranial volume (ICV) and subarachnoid space (SAS) volume. We found a statistically significant decrease in the ICV and an increase in the SAS volume with respect to normal aging.",
keywords = "MRI, Meninges, convolutional networks, deformable models, harmonization, longitudinal study",
author = "Peiyu Duan and Shuo Han and Lianrui Zuo and Yang An and Yihao Liu and Ahmed Alshareef and Junghoon Lee and Aaron Carass and Resnick, {Susan M.} and Prince, {Jerry L.}",
note = "Publisher Copyright: {\textcopyright} 2022 SPIE.; Medical Imaging 2022: Image Processing ; Conference date: 21-03-2021 Through 27-03-2021",
year = "2022",
doi = "10.1117/12.2613146",
language = "English (US)",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Olivier Colliot and Ivana Isgum and Landman, {Bennett A.} and Loew, {Murray H.}",
booktitle = "Medical Imaging 2022",
}