Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions

Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions Quantitative analysis of brain MRI is routine for many neurological diseases and conditions and relies on accurate segmentation of structures of interest. Deep learning-based segmentation approaches for brain MRI are gaining interest due to their self-learning and generalization ability over large amounts of data. As the deep learning architectures are becoming more mature, they gradually outperform previous state-of-the-art classical machine learning algorithms. This review aims to provide an overview of current deep learning-based segmentation approaches for quantitative brain MRI. First we review the current deep learning architectures used for segmentation of anatomical brain structures and brain lesions. Next, the performance, speed, and properties of deep learning approaches are summarized and discussed. Finally, we provide a critical assessment of the current state and identify likely future developments and trends. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Digital Imaging Springer Journals

Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions

Loading next page...
 
/lp/springer_journal/deep-learning-for-brain-mri-segmentation-state-of-the-art-and-future-XcCCy7KoUl

References (74)

Publisher
Springer Journals
Copyright
Copyright © 2017 by The Author(s)
Subject
Medicine & Public Health; Imaging / Radiology
ISSN
0897-1889
eISSN
1618-727X
DOI
10.1007/s10278-017-9983-4
pmid
28577131
Publisher site
See Article on Publisher Site

Abstract

Quantitative analysis of brain MRI is routine for many neurological diseases and conditions and relies on accurate segmentation of structures of interest. Deep learning-based segmentation approaches for brain MRI are gaining interest due to their self-learning and generalization ability over large amounts of data. As the deep learning architectures are becoming more mature, they gradually outperform previous state-of-the-art classical machine learning algorithms. This review aims to provide an overview of current deep learning-based segmentation approaches for quantitative brain MRI. First we review the current deep learning architectures used for segmentation of anatomical brain structures and brain lesions. Next, the performance, speed, and properties of deep learning approaches are summarized and discussed. Finally, we provide a critical assessment of the current state and identify likely future developments and trends.

Journal

Journal of Digital ImagingSpringer Journals

Published: Jun 2, 2017

There are no references for this article.