Format

Send to

Choose Destination
J Digit Imaging. 2017 Aug;30(4):449-459. doi: 10.1007/s10278-017-9983-4.

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

Author information

1
Radiology Informatics Lab, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA.
2
Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA.
3
Radiology Informatics Lab, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA. bje@mayo.edu.

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.

KEYWORDS:

Brain lesion segmentation; Convolutional neural network; Deep learning; Quantitative brain MRI

PMID:
28577131
PMCID:
PMC5537095
DOI:
10.1007/s10278-017-9983-4
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Springer Icon for PubMed Central
Loading ...
Support Center