Artificial intelligence, machine (deep) learning and radio(geno)mics: definitions and nuclear medicine imaging applications

被引：113

作者：

Visvikis, Dimitris ^{[1
]}

Le Rest, Catherine Cheze ^{[2
]}

Jaouen, Vincent ^{[1
]}

Hatt, Mathieu ^{[1
]}

机构：

[1] Univ Brest, Fac Med, IBRBS, LaTIM,INSERM UMR 1101, 22 Ave Camille Desmoulins, F-29238 Brest, France

[2] CHU Miletrie, Nucl Med Dept, Poitiers, France

来源：

EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING | 2019年 / 46卷 / 13期

关键词：

Artificial intelligence; Machine learning; Deep learning; Radiomics; Radiogenomics; CONVOLUTIONAL NEURAL-NETWORKS; ATTENUATION CORRECTION; RADIOMICS; CANCER; SEGMENTATION; IMAGES; PET/CT; CLASSIFICATION; PREDICTION; FEATURES;

D O I：

10.1007/s00259-019-04373-w

中图分类号：

R8 [特种医学]; R445 [影像诊断学];

学科分类号：

1002 ; 100207 ; 1009 ;

摘要：

Techniques from the field of artificial intelligence, and more specifically machine (deep) learning methods, have been core components of most recent developments in the field of medical imaging. They are already being exploited or are being considered to tackle most tasks, including image reconstruction, processing (denoising, segmentation), analysis and predictive modelling. In this review we introduce and define these key concepts and discuss how the techniques from this field can be applied to nuclear medicine imaging applications with a particular focus on radio(geno)mics.

引用

页码：2630 / 2637

页数：8

共 81 条

[1]

Aerts H, SPIE MED IM 2016 COM, DOI [10.1117/12.2214251, DOI 10.1117/12.2214251]

[2] Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach [J].

Aerts, Hugo J. W. L. ;

Velazquez, Emmanuel Rios ;

Leijenaar, Ralph T. H. ;

Parmar, Chintan ;

Grossmann, Patrick ;

Cavalho, Sara ;

Bussink, Johan ;

Monshouwer, Rene ;

Haibe-Kains, Benjamin ;

Rietveld, Derek ;

Hoebers, Frank ;

Rietbergen, Michelle M. ;

Leemans, C. Rene ;

Dekker, Andre ;

Quackenbush, John ;

Gillies, Robert J. ;

Lambin, Philippe .

NATURE COMMUNICATIONS, 2014, 5

[3] 3-D RPET-NET: Development of a 3-D PET Imaging Convolutional Neural Network for Radiomics Analysis and Outcome Prediction [J].

Amyar, A. ;

Ruan, S. ;

Gardin, I. ;

Chatelain, C. ;

Decazes, P. ;

Modzelewski, R. .

IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES, 2019, 3 (02) :225-231

[4] Imaging genomics in cancer research: limitations and promises [J].

Bai, Harrison X. ;

Lee, Ashley M. ;

Yang, Li ;

Zhang, Paul ;

Davatzikos, Christos ;

Maris, John M. ;

Diskin, Sharon J. .

BRITISH JOURNAL OF RADIOLOGY, 2016, 89 (1061)

[5] Innovations in Instrumentation for Positron Emission Tomography [J].

Berg, Eric ;

Cherry, Simon R. .

SEMINARS IN NUCLEAR MEDICINE, 2018, 48 (04) :311-331

[6] Using convolutional neural networks to estimate time-of-flight from PET detector waveforms [J].

Berg, Eric ;

Cherry, Simon R. .

PHYSICS IN MEDICINE AND BIOLOGY, 2018, 63 (02)

[7]

Beyer T, 2000, J NUCL MED, V41, P1369

[8] Deep Learning and Radiomics predict complete response after neo-adjuvant chemoradiation for locally advanced rectal cancer (vol 8, 12611, 2018) [J].

Bibault, Jean-Emmanuel ;

Giraud, Philippe ;

Housset, Martin ;

Durdux, Catherine ;

Taieb, Julien ;

Berger, Anne ;

Coriat, Romain ;

Chaussade, Stanislas ;

Dousset, Bertrand ;

Nordlinger, Bernard ;

Burgun, Anita .

SCIENTIFIC REPORTS, 2018, 8

[9] Automatic lesion detection and segmentation of 18F-FET PET in gliomas: A full 3D U-Net convolutional neural network study [J].

Blanc-Durand, Paul ;

Van der Gucht, Axel ;

Schaefer, Niklaus ;

Itti, Emmanuel ;

Prior, John O. .

PLOS ONE, 2018, 13 (04)

[10] Bladder Cancer Treatment Response Assessment in CT using Radiomics with Deep-Learning [J].

Cha, Kenny H. ;

Hadjiiski, Lubomir ;

Chan, Heang-Ping ;

Weizer, Alon Z. ;

Alva, Ajjai ;

Cohan, Richard H. ;

Caoili, Elaine M. ;

Paramagul, Chintana ;

Samala, Ravi K. .

SCIENTIFIC REPORTS, 2017, 7

← 1 2 3 4 5 6 7 8 9 →