Using deep learning techniques in medical imaging: a systematic review of applications on CT and PET

Medical imaging is a rich source of invaluable information necessary for clinical judgements. However, the analysis of those exams is not a trivial assignment. In recent times, the use of deep learning (DL) techniques, supervised or unsupervised, has been empowered and it is one of the current resea...

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Bibliographic Details
Published in:The Artificial intelligence review 2020-08, Vol.53 (6), p.4093-4160
Main Authors: Domingues, Inês, Pereira, Gisèle, Martins, Pedro, Duarte, Hugo, Santos, João, Abreu, Pedro Henriques
Format: Article
Language:English
Subjects:
Artificial Intelligence
Computed tomography
Computer Science
CT imaging
Deep learning
Emission analysis
Image analysis
Learning strategies
Machine learning
Medical imaging
Medical imaging equipment
Medical research
PET imaging
Positron emission
Surveys
Systematic review
Tomography
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Summary:Medical imaging is a rich source of invaluable information necessary for clinical judgements. However, the analysis of those exams is not a trivial assignment. In recent times, the use of deep learning (DL) techniques, supervised or unsupervised, has been empowered and it is one of the current research key areas in medical image analysis. This paper presents a survey of the use of DL architectures in computer-assisted imaging contexts, attending two different image modalities: the actively studied computed tomography and the under-studied positron emission tomography, as well as the combination of both modalities, which has been an important landmark in several decisions related to numerous diseases. In the making of this review, we analysed over 180 relevant studies, published between 2014 and 2019, that are sectioned by the purpose of the research and the imaging modality type. We conclude by addressing research issues and suggesting future directions for further improvement. To our best knowledge, there is no previous work making a review of this issue.
ISSN:0269-2821
1573-7462
DOI:10.1007/s10462-019-09788-3