@inproceedings{491b419b77b44ef2b15ddf3cf5cc9931,
title = "Large-scale parcellation of the ventricular system using convolutional neural networks",
abstract = "Enlarged ventricles are a marker of several brain diseases; however, they are also associated with normal aging. Better understanding of the distribution of ventricular sizes in a large population would be of great clinical value to robustly define imaging markers that distinguish health and disease. The AGES-Reykjavik study includes magnetic resonance imaging scans of 4811 individuals from an elderly Icelandic population. Automated brain segmentation algorithms are necessary to analyze such a large data set but state-of-the-art algorithms often require long processing times or depend on large manually annotated data sets when based on deep learning approaches. In an effort to increase robustness, decrease processing time, and avoid tedious manual delineations, we selected 60 subjects with a large range of ventricle sizes and generated training labels using an automated whole brain segmentation algorithm designed for brains with ventriculomegaly. Lesion labels were added to the training labels, which were subsequently used to train a patch-based three-dimensional U-net Convolutional Neural Network for very fast and robust labeling of the remaining subjects. Comparisons with ground truth manual labels demonstrate that the proposed method yields robust segmentation and labeling of the four main sub-compartments of the ventricular system.",
keywords = "Brain MRI, Convolutional Neural Networks, Enlarged Ventricles, Segmentation",
author = "Atlason, {Hans E.} and Muhan Shao and Vidar Robertsson and Sigurdur Sigurdsson and Vilmundur Gudnason and Prince, {Jerry L.} and Ellingsen, {Lotta M.}",
note = "Funding Information: This work was supported by RANNIS (The Icelandic Centre for Research) through grant 173942-051 and by the NIH/NINDS under grant R21-NS096497. Publisher Copyright: {\textcopyright} 2019 SPIE.; Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging ; Conference date: 19-02-2019 Through 21-02-2019",
year = "2019",
doi = "10.1117/12.2514590",
language = "English (US)",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Barjor Gimi and Andrzej Krol",
booktitle = "Medical Imaging 2019",
}