Synergizing medical imaging and radiotherapy with deep learning

被引：29

作者：

Shan, Hongming ^{[1
]}

Jia, Xun ^{[2
]}

Yan, Pingkun ^{[1
]}

Li, Yunyao ^{[3
]}

Paganetti, Harald ^{[4
,5
]}

Wang, Ge ^{[1
]}

机构：

[1] Rensselaer Polytech Inst, Dept Biomed Engn, Troy, NY 12180 USA

[2] UT Southwestern Med Ctr, Dept Radiat Oncol, Dallas, TX 75390 USA

[3] IBM Res, San Jose, CA 95120 USA

[4] Massachusetts Gen Hosp, Dept Radiat Oncol, Boston, MA 02114 USA

[5] Harvard Med Sch, Boston, MA 02114 USA

来源：

MACHINE LEARNING-SCIENCE AND TECHNOLOGY | 2020年 / 1卷 / 02期

关键词：

LOW-DOSE CT; GENERATIVE ADVERSARIAL NETWORK; CONVOLUTIONAL NEURAL-NETWORK; KNOWLEDGE-BASED PREDICTION; BRAIN-TUMOR SEGMENTATION; LUNG-CANCER; AT-RISK; RECONSTRUCTION; RADIOMICS; IMAGES;

D O I：

10.1088/2632-2153/ab869f

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This article reviews deep learning methods for medical imaging (focusing on image reconstruction, segmentation, registration, and radiomics) and radiotherapy (ranging from planning and verification to prediction) as well as the connections between them. Then, future topics are discussed involving semantic analysis through natural language processing and graph neural networks. It is believed that deep learning in particular, and artificial intelligence and machine learning in general, will have a revolutionary potential to advance and synergize medical imaging and radiotherapy for unprecedented smart precision healthcare.

引用

页数：25

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