Deep learning in medical imaging and radiation therapy

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...

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Veröffentlicht in:Medical physics (Lancaster) Jg. 46; H. 1; S. e1 - e36
Hauptverfasser: Sahiner, Berkman, Pezeshk, Aria, Hadjiiski, Lubomir M., Wang, Xiaosong, Drukker, Karen, Cha, Kenny H., Summers, Ronald M., Giger, Maryellen L.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States John Wiley and Sons Inc 01.01.2019
Schlagworte:
computer‐aided detection/characterization
Deep Learning
deep learning, machine learning
Diagnostic Imaging - methods
Humans
Image Processing, Computer-Assisted
Radiotherapy - methods
reconstruction
Review
segmentation
Signal-To-Noise Ratio
treatment
treatment
reconstruction
segmentation
deep learning, machine learning
computer-aided detection/characterization
ISSN:0094-2405, 2473-4209, 2473-4209
Online-Zugang:Volltext
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
ObjectType-Review-3
content type line 23
ISSN:0094-2405
2473-4209
2473-4209
DOI:10.1002/mp.13264