Cranial meninges reconstruction based on convolutional networks and deformable models: applications to longitudinal study of normal aging

Peiyu Duan; Shuo Han; Lianrui Zuo; Yang An; Yihao Liu; Ahmed Alshareef; Junghoon Lee; Aaron Carass; Susan M. Resnick; Jerry L. Prince

doi:10.1117/12.2613146

4 April 2022 Cranial meninges reconstruction based on convolutional networks and deformable models: applications to longitudinal study of normal aging

Peiyu Duan, Shuo Han, Lianrui Zuo, Yang An, Yihao Liu, Ahmed Alshareef, Junghoon Lee, Aaron Carass, Susan M. Resnick, Jerry L. Prince

Author Affiliations +

Proceedings Volume 12032, Medical Imaging 2022: Image Processing; 1203215 (2022) https://doi.org/10.1117/12.2613146
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

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.

Conference Presentation

Citation Download Citation

Peiyu Duan, Shuo Han, Lianrui Zuo, Yang An, Yihao Liu, Ahmed Alshareef, Junghoon Lee, Aaron Carass, Susan M. Resnick, and Jerry L. Prince "Cranial meninges reconstruction based on convolutional networks and deformable models: applications to longitudinal study of normal aging", Proc. SPIE 12032, Medical Imaging 2022: Image Processing, 1203215 (4 April 2022); https://doi.org/10.1117/12.2613146

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