Format

Send to

Choose Destination
Med Phys. 2019 Jan;46(1):e1-e36. doi: 10.1002/mp.13264. Epub 2018 Nov 20.

Deep learning in medical imaging and radiation therapy.

Author information

1
DIDSR/OSEL/CDRH U.S. Food and Drug Administration, Silver Spring, MD, 20993, USA.
2
Department of Radiology, University of Michigan, Ann Arbor, MI, 48109, USA.
3
Imaging Biomarkers and Computer-aided Diagnosis Lab, Radiology and Imaging Sciences, NIH Clinical Center, Bethesda, MD, 20892-1182, USA.
4
Department of Radiology, University of Chicago, Chicago, IL, 60637, USA.

Abstract

The goals of this review paper on deep learning (DL) in medical imaging and radiation therapy are to (a) summarize what has been achieved to date; (b) identify common and unique challenges, and strategies that researchers have taken to address these challenges; and (c) identify some of the promising avenues for the future both in terms of applications as well as technical innovations. We introduce the general principles of DL and convolutional neural networks, survey five major areas of application of DL in medical imaging and radiation therapy, identify common themes, discuss methods for dataset expansion, and conclude by summarizing lessons learned, remaining challenges, and future directions.

KEYWORDS:

computer-aided detection/characterization; deep learning, machine learning; reconstruction; segmentation; treatment

PMID:
30367497
DOI:
10.1002/mp.13264
[Indexed for MEDLINE]
Free full text

Supplemental Content

Full text links

Icon for Wiley Icon for MLibrary (Deep Blue)
Loading ...
Support Center