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山东大学学报 (医学版) ›› 2023, Vol. 61 ›› Issue (12): 21-29.doi: 10.6040/j.issn.1671-7554.0.2023.0774

• 医学影像人工智能的创新与挑战—专家综述 • 上一篇    下一篇

深度学习在医学影像学中的研究现状及发展前景

林冰洁1,2,王梅云1,2,3,*()   

  1. 1. 郑州大学人民医院医学影像科，河南 郑州 450003
    2. 河南省人民医院医学影像科，河南 郑州 450003
    3. 河南省科学院生物医学研究所 脑科学与类脑智能实验室，河南 郑州 450003
  • 收稿日期:2023-09-01 出版日期:2023-12-10 发布日期:2024-01-11
  • 通讯作者: 王梅云 E-mail:mywang@ha.edu.cnm
  • 作者简介:王梅云，主任医师、二级教授、博士研究生导师，河南省人民医院副院长、医学影像研究所所长、医学影像科主任，河南省科学院生物医学研究所所长、郑州大学医学技术学院学术副院长，美国哈佛大学医学院引进回国人才，长期致力于医学影像新技术研发及在重大疾病诊疗中的转化。学术兼职：中国放射学界首位美国医学与生物工程院(AIMBE)Fellow、国际医学磁共振学会(ISMRM)Fellow(均为2022年唯一入选的中国医生)，国内首位国际神经血管疾病学会(ISNVD)主席、国际医学磁共振学会(ISMRM)理事会理事及精神成像学组主席；中华医学会放射学分会常务委员、国际交流工作组组长及神经学组副组长，河南省放射学会候任主任委员等。荣誉称号：获得国家百千万人才工程“有突出贡献中青年专家”、国务院政府特殊津贴专家、全国科技系统抗击新冠肺炎疫情先进个人、中原学者、河南省优秀专家等荣誉称号。在国际顶级期刊JAMA、Nature Medicine、Nature Genetics、Nature Communications和放射学领域排名第一期刊Radiology等权威期刊上发表论文300余篇，3篇论文入选全球Top 1% ESI高被引论文；主持国家重点研发计划“诊疗装备与生物医用材料”重点专项及政府间合作重点专项、国家自然科学基金重点国际合作项目、面上项目等科研项目19项；牵头发布全国团体标准7项；以第一发明人获得授权美国发明专利和中国国家发明专利13项；获得河南省科技进步奖一等奖3项、中国发明创业奖创新奖一等奖1项和国际华人医学磁共振学会突出贡献奖等奖项；受邀在国际一流会议或学术机构如哈佛大学、剑桥大学等做特邀英文讲座50余次
  • 基金资助:
    河南省医学科技攻关计划项目(SBGJ202101002)

Research status and development prospect of deep learning in medical imaging

Bingjie LIN1,2,Meiyun WANG1,2,3,*()   

  1. 1. Department of Medical Imaging, Zhengzhou University People's Hospital, Zhengzhou 450003, Henan, China
    2. Department of Medical Imaging, Henan Provincial People's Hospital, Zhengzhou 450003, Henan, China
    3. Laboratory of Brain Science and Brain-like Intelligence, Biomedical Research Institute, Henan Academy of Sciences, Zhengzhou 450003, Henan, China
  • Received:2023-09-01 Online:2023-12-10 Published:2024-01-11
  • Contact: Meiyun WANG E-mail:mywang@ha.edu.cnm

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摘要：

精准医疗，影像先行；精准影像，技术先行。近年来，随着人工智能的迅速发展，深度学习作为其重要分支，已广泛应用于信号处理、计算机视觉和自然语言处理等诸多领域，其中基于深度学习的医学影像数据分割、疾病检测及预后预测等已成为众多学者研究的热点。本文将简要概述深度学习在医学影像学主要技术领域的应用现状，并分析其在医学影像学临床应用中所面临的挑战与发展前景，旨在为深度学习算法的临床转化提供参考。

关键词: 深度学习, 医学影像学, 研究现状, 发展前景

Abstract:

Precision medicine, imaging first; precision imaging, technology first. In recent years, with the rapid development of artificial intelligence, deep learning, as an important branch, has been widely used in many fields such as signal processing, computer vision and natural language processing, etc., among which medical image data segmentation, disease detection and prognosis prediction based on deep learning have become the hot spots of many scholars' research. In this paper, we will briefly outline the current status of deep learning application in the main technical fields of medical imaging, and analyze the challenges and development prospects of its clinical application in medical imaging, aiming to provide reference for the transformation of deep learning algorithms in the clinic.

Key words: Deep learning, Medical imaging, Research status, Development prospect

中图分类号: 

  • R445
1 Ensmenger N . Is chess the drosophila of artificial intelligence? A social history of an algorithm[J]. Soc Stud Sci, 2012, 42 (1): 5- 30.
doi: 10.1177/0306312711424596
2 袁灵, 成思航, 苏童, 等. 医学影像学的研究进展综述[J]. 中国科学: 生命科学, 2021, 51 (8): 1130- 1139.
YUAN Ling , CHENG Sihang , SU Tong , et al. A review of the research progress of medical imaging (in Chinese)[J]. Sci Sin Vitae, 2021, 51 (8): 1130- 1139.
3 Mazurowski MA , Buda M , Saha A , et al. Deep learning in radiology: an overview of the concepts and a survey of the state of the art with focus on MRI[J]. J Magn Reson Imaging, 2019, 49 (4): 939- 954.
doi: 10.1002/jmri.26534
4 Chan HP , Samala RK , Hadjiiski LM , et al. Deep learning in medical image analysis[J]. J Magn Reson Imaging, 2020, 1213, 3- 21.
doi: 10.1007/978-3-030-33128-3_1
5 Karar ME , Hemdan EED , Shouman MA . Cascaded deep learning classifiers for computer-aided diagnosis of COVID-19 and pneumonia diseases in X-ray scans[J]. Complex Intell Systems, 2021, 7 (1): 235- 247.
doi: 10.1007/s40747-020-00199-4
6 Erickson BJ , Korfiatis P , Akkus Z , et al. Machine learning for medical imaging[J]. Radiographics, 2017, 37 (2): 505- 515.
doi: 10.1148/rg.2017160130
7 Khan FA , Majidulla A , Tavaziva G , et al. Chest x-ray analysis with deep learning-based software as a triage test for pulmonary tuberculosis: a prospective study of diagnostic accuracy for culture-confirmed disease[J]. The Lancet Digit Health, 2020, 2 (11): e573- e581.
doi: 10.1016/S2589-7500(20)30221-1
8 Wang GY , Liu XH , Shen J , et al. A deep-learning pipeline for the diagnosis and discrimination of viral, non-viral and COVID-19 pneumonia from chest X-ray images[J]. Nat Biomed Eng, 2021, 5 (6): 509- 521.
doi: 10.1038/s41551-021-00704-1
9 Weiss J , Raghu VK , Bontempi D , et al. Deep learning to estimate lung disease mortality from chest radiographs[J]. Nat Commun, 2023, 14 (1): 2797.
doi: 10.1038/s41467-023-37758-5
10 Cicero M , Bilbily A , Colak E , et al. Training and validating a deep convolutional neural network for computer-aided detection and classification of abnormalities on frontal chest radiographs[J]. Invest Radiol, 2017, 52 (5): 281- 287.
doi: 10.1097/RLI.0000000000000341
11 Seah JCY , Tang CHM , Buchlak QD , et al. Effect of a comprehensive deep-learning model on the accuracy of chest X-ray interpretation by radiologists: a retrospective, multireader multicase study[J]. The Lancet Digit Health, 2021, 3 (8): e496- e506.
doi: 10.1016/S2589-7500(21)00106-0
12 Hosny A , Parmar C , Quackenbush J , et al. Artificial intelligence in radiology[J]. Nat Rev Cancer, 2018, 18 (8): 500- 510.
doi: 10.1038/s41568-018-0016-5
13 Lehman CD , Yala A , Schuster T , et al. Mammographic breast density assessment using deep learning: clinical implementation[J]. Radiology, 2019, 290 (1): 52- 58.
doi: 10.1148/radiol.2018180694
14 Vachon CM , Scott CG , Norman AD , et al. Impact of artificial intelligence system and volumetric density on risk prediction of interval, screen-detected, and advanced breast cancer[J]. J Clin Oncol, 2023, 41 (17): 3172- 3183.
doi: 10.1200/JCO.22.01153
15 欧阳汝珊, 李霖, 林小慧, 等. 基于乳腺X线摄影的深度学习技术鉴别乳腺影像报告和数据系统3类与4类疾病的价值[J]. 中华放射学杂志, 2023, 57 (2): 166- 172.
OUYANG Rushan , LI Lin , LIN Xiaohui , et al. The value of deep learning technology based on mammography in differentiating breast imaging reporting and data system category 3 and 4 lesions[J]. Chinese Journal of Radiology, 2023, 57 (2): 166- 172.
16 彭卫军, 顾雅佳, 龚敬. 人工智能在乳腺肿瘤影像中的应用现状及展望[J]. 中华放射学杂志, 2023, 57 (2): 121- 124.
PENG Weijun , GU Yajia , GONG Jing . Application prospects of artificial intelligence technology in breast imaging[J]. Chinese Journal of Radiology, 2023, 57 (2): 121- 124.
17 黄泽青, 刘予豪, 方汉军, 等. 基于深度迁移学习模型实现股骨头坏死与其他髋部疾病的X线片鉴别诊断[J]. 中华骨科杂志, 2023, 43 (1): 72- 80.
HUANG Zeqing , LIU Yuhao , FANG Hanjun , et al. A deep transfer learning method using plain radiographs for the differential diagnosis of osteonecrosis of the femoral head with other hip diseases[J]. Chinese Journal of Orthopaedics, 2023, 43 (1): 72- 80.
18 Cheng CT , Wang YR , Chen HW , et al. A scalable physician-level deep learning algorithm detects universal trauma on pelvic radiographs[J]. Nat Commun, 2021, 12 (1): 1066.
doi: 10.1038/s41467-021-21311-3
19 Mao LT , Xia ZQ , Pan L , et al. Deep learning for screening primary osteopenia and osteoporosis using spine radiographs and patient clinical covariates in a Chinese population[J]. Front Endocrinol, 2022, 13, 971877.
doi: 10.3389/fendo.2022.971877
20 Al Arif S M M R , Knapp K , Slabaugh G . Fully automatic cervical vertebrae segmentation framework for X-ray images[J]. Computer Methods and Programs in Biomedicine, 2018, 157, 95- 111.
doi: 10.1016/j.cmpb.2018.01.006
21 Venkadesh KV , Setio AAA , Schreuder A , et al. Deep learning for malignancy risk estimation of pulmonary nodules detected at low-dose screening CT[J]. Radiology, 2021, 300 (2): 438- 447.
doi: 10.1148/radiol.2021204433
22 Bianconi F , Fravolini M L , Pizzoli S , et al. Comparative evaluation of conventional and deep learning methods for semi-automated segmentation of pulmonary nodules on CT[J]. Quant Imaging Med Surg, 2021, 11 (7): 3286- 3305.
doi: 10.21037/qims-20-1356
23 Wang T , Lei Y , Tian Z , et al. Deep learning-based image quality improvement for low-dose computed tomography simulation in radiation therapy[J]. J Med Imaging, 2019, 6 (4): 043504.
doi: 10.1117/1.JMI.6.4.043504
24 Baguer DO , Leuschner J , Schmidt M . Computed tomography reconstruction using deep image prior and learned reconstruction methods[J]. Inverse Probl, 2020, 36 (9): 094004.
doi: 10.1088/1361-6420/aba415
25 Zavala-Mondragon LA , Rongen P , Bescos JO , et al. Noise reduction in CT using learned wavelet-frame shrinkage networks[J]. IEEE Trans Med Imaging, 2022, 41 (8): 2048- 2066.
doi: 10.1109/TMI.2022.3154011
26 Leuschner J , Schmidt M , Ganguly P S , et al. Quantitative comparison of deep learning-based image reconstruction methods for low-dose and sparse-angle CT applications[J]. J Imaging, 2021, 7 (3): 44.
doi: 10.3390/jimaging7030044
27 Hata A , Yanagawa M , Yoshida Y , et al. Combination of deep learning-based denoising and iterative reconstruction for ultra-low-dose ct of the chest: image quality and lung-RADS evaluation[J]. AJR Am J Roentgenol, 2020, 215 (6): 1321- 1328.
doi: 10.2214/AJR.19.22680
28 Yeoh H , Hong SH , Ahn C , et al. Deep learning algorithm for simultaneous noise reduction and edge sharpening in low-dose CT images: a pilot study using lumbar spine CT[J]. Korean J Radiol, 2021, 22 (11): 1850- 1857.
doi: 10.3348/kjr.2021.0140
29 王金华, 宋兰, 隋昕, 等. 深度学习重建改善胸部低剂量CT图像质量的价值[J]. 中华放射学杂志, 2022, 56 (1): 74- 80.
WANG Jinhua , SONG Lan , SUI Xin , et al. Application value of deep learning reconstruction to improve image quality of low-dose chest CT[J]. Chinese Journal of Radiology, 2022, 56 (1): 74- 80.
30 Chilamkurthy S , Ghosh R , Tanamala S , et al. Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study[J]. Lancet, 2018, 392 (10162): 2388- 2396.
doi: 10.1016/S0140-6736(18)31645-3
31 Mu D , Bai JJ , Chen WP , et al. Calcium scoring at coronary CT angiography using deep learning[J]. Radiology, 2022, 302 (2): 309- 316.
doi: 10.1148/radiol.2021211483
32 Yasaka K , Akai H , Abe O , et al. Deep learning with convolutional neural network for differentiation of liver masses at dynamic contrast-enhanced CT: a preliminary study[J]. Radiology, 2018, 286 (3): 887- 896.
doi: 10.1148/radiol.2017170706
33 Jin L , Yang JC , Kuang KM , et al. Deep-learning-assisted detection and segmentation of rib fractures from CT scans: Development and validation of FracNet[J]. EBioMedicine, 2020, 62, 103106.
doi: 10.1016/j.ebiom.2020.103106
34 Zhou QQ , Wang JS , Tang W , et al. Automatic detection and classification of rib fractures on thoracic ct using convolutional neural network: accuracy and feasibility[J]. Korean J Radiol, 2020, 21 (7): 869- 879.
doi: 10.3348/kjr.2019.0651
35 Weikert T , Noordtzij LA , Bremerich J , et al. Assessment of a Deep Learning Algorithm for the Detection of Rib Fractures on Whole-Body Trauma Computed Tomography[J]. Korean J Radiol, 2020, 21 (7): 891- 899.
doi: 10.3348/kjr.2019.0653
36 Mawlawi O , Townsend DW . Multimodality imaging: an update on PET/CT technology[J]. Eur J Nucl Med Mol, 2009, 36 (S1): 15- 29.
doi: 10.1007/s00259-008-1016-6
37 Wallis D , Soussan M , Lacroix M , et al. Correction to: An[18F]FDG-PET/CT deep learning method for fully automated detection of pathological mediastinal lymph nodes in lung cancer patients[J]. Eur J Nucl Med Mol, 2022, 49 (10): 3598.
doi: 10.1007/s00259-022-05855-0
38 Capobianco N , Meignan M , Cottereau AS , et al. Deep-learning 18F-FDG uptake classification enables total metabolic tumor volume estimation in diffuse large B-Cell lymphoma[J]. J Nucl Med, 2021, 62 (1): 30- 36.
doi: 10.2967/jnumed.120.242412
39 Mu W , Jiang L , Zhang JY , et al. Non-invasive decision support for NSCLC treatment using PET/CT radiomics[J]. Nature Communications, 2020, 11 (1): 5228.
doi: 10.1038/s41467-020-19116-x
40 王梅云. 磁共振成像人工智能的研究现状及发展前景[J]. 磁共振成像, 2023, 14 (3): 1- 5.
WANG Meiyun . Research status and development prospect of magnetic resonance imaging artificial intelligence[J]. Chinese Journal of Magnetic Resonance Imaging, 2023, 14 (3): 1- 5.
41 Wang SS , Su ZH , Ying L , et al. Accelerating magnetic resonance imaging via deep learning[J]. Proc IEEE Int Symp Biomed Imaging, 2016, 514- 517.
doi: 10.1109/ISBI.2016.7493320
42 Zhang F , Wells WM , O'Donnell LJ . Deep diffusion mri registration (DDMReg): a deep learning method for diffusion mri registration[J]. IEEE Trans Med Imaging, 2022, 41 (6): 1454- 1467.
doi: 10.1109/TMI.2021.3139507
43 Hegi ME , Diserens AC , Gorlia T , et al. MGMT gene silencing and benefit from temozolomide in glioblastoma[J]. N Engl J Med, 2005, 352 (10): 997- 1003.
doi: 10.1056/NEJMoa043331
44 薛彩强, 杜晓灏, 金龙, 等. 基于MRI深度学习模型预测WHOⅡ、Ⅲ级胶质瘤MGMT启动子甲基化状态[J]. 中华放射学杂志, 2021, 55 (7): 734- 738.
Xue CQ , Du XH , Jin L , et al. Prediction of methylation status of MGMT promoter in WHO grade Ⅱ, Ⅲ glioma based on MRI deep learning model[J]. Chinese Journal of Radiology, 2021, 55 (7): 734- 738.
45 Moradiya Y , Janjua N . Presentation and outcomes of "wake-up strokes" in a large randomized stroke trial: analysis of data from the international stroke trial[J]. J Stroke Cerebrovasc Dis, 2013, 22 (8): e286- e292.
doi: 10.1016/j.jstrokecerebrovasdis.2012.07.016
46 姜亮, 周蕾蕾, 艾中萍, 等. 基于DWI和FLAIR的深度学习预测急性脑卒中发病时间[J]. 中华放射学杂志, 2021, 55 (8): 811- 816.
JIANG L , ZHOU LL , AI ZP , et al. Prediction of the onset time of acute stroke by deep learning based on DWI and FLAIR[J]. Chinese Journal of Radiology, 2021, 55 (8): 811- 816.
47 Katabathula S , Wang Q , Xu R . Predict Alzheimer's disease using hippocampus MRI data: a lightweight 3D deep convolutional network model with visual and global shape representations[J]. Alzheimers Res Ther, 2021, 13 (1): 104.
doi: 10.1186/s13195-021-00837-0
48 马璇, 赵世华. 机器学习在心脏磁共振成像中的应用进展[J]. 中华心血管病杂志, 2023, 51 (4): 434- 439.
MA Xuan , ZHAO Shihua . Progress in the clinical application of machine learning in cardiac magnetic resonance imaging[J]. Chinese Journal of Cardiology, 2023, 51 (4): 434- 439.
49 Qin C , Schlemper J , Caballero J , et al. Convolutional recurrent neural networks for dynamic mr image reconstruction[J]. IEEE Trans Med Imaging, 2019, 38 (1): 280- 290.
doi: 10.1109/TMI.2018.2863670
50 Fahmy AS , Rowin EJ , Chan RH , et al. Improved quantification of myocardium scar in late gadolinium enhancement images: deep learning based image fusion approach[J]. J Magn Reson Imaging, 2021, 54 (1): 303- 312.
doi: 10.1002/jmri.27555
51 Zhang Q , Burrage MK , Lukaschuk E , et al. Toward replacing late gadolinium enhancement with artificial intelligence virtual native enhancement for gadolinium-free cardiovascular magnetic resonance tissue characterization in hypertrophic cardiomyopathy[J]. Circulation, 2021, 144 (8): 589- 599.
doi: 10.1161/CIRCULATIONAHA.121.054432
52 Zucker EJ , Sandino CM , Kino A , et al. Free-breathing accelerated cardiac MRI using deep learning: validation in children and young adults[J]. Radiology, 2021, 300 (3): 539- 548.
doi: 10.1148/radiol.2021202624
53 Masutani EM , Bahrami N , Hsiao A . Deep learning single-frame and multiframe super-resolution for cardiac MRI[J]. Radiology, 2020, 295 (3): 552- 561.
doi: 10.1148/radiol.2020192173
54 Sung H , Ferlay J , Siegel RL , et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin, 2021, 71 (3): 209- 249.
doi: 10.3322/caac.21660
55 Raman AG , Jones C , Weiss CR . Machine learning for hepatocellular carcinoma segmentation at mri: radiology in training[J]. Radiology, 2022, 304 (3): 509- 515.
doi: 10.1148/radiol.212386
56 Zhao JF , Li DW , Xiao XJ , et al. United adversarial learning for liver tumor segmentation and detection of multi-modality non-contrast MRI[J]. Med Image Anal, 2021, 73, 102154.
doi: 10.1016/j.media.2021.102154
57 Hamm CA , Wang CJ , Savic LJ , et al. Deep learning for liver tumor diagnosis part Ⅰ: development of a convolutional neural network classifier for multi-phasic MRI[J]. Eur Radiol, 2019, 29 (7): 3338- 3347.
doi: 10.1007/s00330-019-06205-9
58 Almansour H , Herrmann J , Gassenmaier S , et al. Deep learning reconstruction for accelerated spine MRI: prospective analysis of interchangeability[J]. Radiology, 2023, 306 (3): e212922.
doi: 10.1148/radiol.212922
59 Sun S , Tan ET , Mintz DN , et al. Evaluation of deep learning reconstructed high-resolution 3D lumbar spine MRI[J]. Eur Radiol, 2022, 32 (9): 6167- 6177.
doi: 10.1007/s00330-022-08708-4
60 Schelb P , Kohl S , Radtke JP , et al. Classification of cancer at prostate MRI: deep learning versus clinical PI-RADS assessment[J]. Radiology, 2019, 293 (3): 607- 617.
doi: 10.1148/radiol.2019190938
61 Zhu Y , Wei R , Gao G , et al. Fully automatic segmentation on prostate MR images based on cascaded fully convolution network[J]. J Magn Reson Imaging, 2019, 49 (4): 1149- 1156.
doi: 10.1002/jmri.26337
62 Diaz M , Peabody JO , Kapoor V , et al. Oncologic outcomes at 10 years following robotic radical prostatectomy[J]. Eur Urol, 2015, 67 (6): 1168- 1176.
doi: 10.1016/j.eururo.2014.06.025
63 Yan Y , Shao L , Liu Z , et al. Deep learning with quantitative features of magnetic resonance images to predict biochemical recurrence of radical prostatectomy: a multi-center study[J]. Cancers, 2021, 13 (12): 3098.
doi: 10.3390/cancers13123098
64 窦猛, 陈哲彬, 王辛, 等. 基于深度学习的多模态医学图像分割综述[J/OL]. 计算机应用, 2023: 1-14. (2023-05-16). https://kns.cnki.net/kcms/detail/51.1307.tp.20230515.1739.002.html
65 Du SJ , Yuan C , Zhou QM , et al. Deep learning-based PET/MR radiomics for the classification of annualized relapse rate in multiple sclerosis[J]. Mult Scler Relat Disord, 2023, 75, 104750.
doi: 10.1016/j.msard.2023.104750
66 Choi JH , Kim HA , Kim W , et al. Early prediction of neoadjuvant chemotherapy response for advanced breast cancer using PET/MRI image deep learning[J]. Sci Rep, 2020, 10 (1): 21149.
doi: 10.1038/s41598-020-77875-5
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