Large-scale parcellation of the ventricular system using convolutional neural networks

Hans E. Atlason, Muhan Shao, Vidar Robertsson, Sigurdur Sigurdsson, Vilmundur Gudnason, Jerry L. Prince, Lotta M. Ellingsen

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

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.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2019
Subtitle of host publicationBiomedical Applications in Molecular, Structural, and Functional Imaging
EditorsBarjor Gimi, Andrzej Krol
PublisherSPIE
ISBN (Electronic)9781510625532
DOIs
StatePublished - Jan 1 2019
EventMedical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging - San Diego, United States
Duration: Feb 19 2019Feb 21 2019

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume10953
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging
CountryUnited States
CitySan Diego
Period2/19/192/21/19

Keywords

  • Brain MRI
  • Convolutional Neural Networks
  • Enlarged Ventricles
  • Segmentation

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Biomaterials
  • Radiology Nuclear Medicine and imaging

Fingerprint Dive into the research topics of 'Large-scale parcellation of the ventricular system using convolutional neural networks'. Together they form a unique fingerprint.

  • Cite this

    Atlason, H. E., Shao, M., Robertsson, V., Sigurdsson, S., Gudnason, V., Prince, J. L., & Ellingsen, L. M. (2019). Large-scale parcellation of the ventricular system using convolutional neural networks. In B. Gimi, & A. Krol (Eds.), Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging [109530N] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 10953). SPIE. https://doi.org/10.1117/12.2514590