医学影像人工智能新进展

贾凯丽; 王雪梅; 贾凯丽; 王雪梅

doi:10.3760/cma.j.issn.1673-4114.2020.01.007

[1]

金征宇. 人工智能医学影像应用: 现实与挑战[J]. 放射学实践, 2018, 33(10): 989-991. doi: 10.13609/j.cnki.1000-0313.2018.10.001
Jin ZY. AI medical imaging applications: reality and challenges[J]. Radiol PractRadiol Pract, 2018, 33(10): 989-991. doi: 10.13609/j.cnki.1000-0313.2018.10.001

[2]

梁长虹, 刘再毅. 人工智能与医学影像再思考[J]. 中华医学信息导报, 2017, 32(22): 21-. doi: 10.3969/j.issn.1000-8039.2017.22.023
Liang CH, Liu ZY. Rethinking artificial intelligence and medical imaging[J]. China Med NewsChina Med News, 2017, 32(22): 21-. doi: 10.3969/j.issn.1000-8039.2017.22.023

[3]

沈旭东. 基于深度学习的时间序列算法综述[J]. 信息技术与信息化, 2019, 226(1): 71-76. doi: 10.3969/j.issn.1672-9528.2019.01.021
Shen XD. Survey of time series algorithms based on deep learning[J]. Infor Technol InformatizationInfor Technol Informatization, 2019, 226(1): 71-76. doi: 10.3969/j.issn.1672-9528.2019.01.021

[4]

陈真诚, 倪利莉, 王红艳, 等. 人工智能技术在医学影像专家系统中的应用及发展[J]. 国外医学·生物医学工程分册, 2001, 24(5): 201−206. DOI: 10.3760/cma.j.issn.1673−4181.2001.05.003.

Chen ZC, Ni LL, Wang HY, et al. Application and development of artificial intelligence technology in medical imaging expert system[J]. Foreign Med Sci (Biomed Eng Fasc), 2001, 24(5): 201−206. DOI: 10.3760/cma.j.issn.1673−4181.2001.05.003.

[5]

高歌, 马帅, 王霄英. 计算机辅助诊断在医学影像诊断中的基本原理和应用进展[J]. 放射学实践, 2016, 31(12): 1127-1129. doi: 10.13609/j.cnki.1000-0313.2016.12.004
Gao G, Ma S, Wang XY. Basic principles and application progress of computer-aided diagnosis in medical imaging diagnosis[J]. Radiol PractRadiol Pract, 2016, 31(12): 1127-1129. doi: 10.13609/j.cnki.1000-0313.2016.12.004

[6]

国务院. 国务院关于印发新一代人工智能发展规划的通知(国发(2017)35号)[EB/OL]. [2019-11-12]. http://www.gov.cn/zhengce/content/2017-07/20/content_5211996.htm.

State Council. Notice of the State Council on Printing and Distributing the New Generation Artificial Intelligence Development Plan (Guofa (2017) No. 35)[EB/OL]. [2019-11-12]. http://www.gov.cn/zhengce/content/2017-07/20/content_5211996.htm.

[7]

Yang W, Chen YY, Liu YB, et al. Cascade of multi-scale convolutional neural networks for bone suppression of chest radiographs in gradient domain[J]. Med Image AnalMed Image Anal, 2017, 35(1): 421-433. doi: 10.1016/j.media.2016.08.004

[8]

Heo SJ, Kim Y, Yun S, et al. Deep learning algorithms with demographic information help to detect tuberculosis in chest radiographs in annual workers' health examination data[J/OL]. Int J Environ Res Public Health, 2019, 16(2): e250[2019-11-12]. https://www.mdpi.com/1660-4601/16/2/250. DOI: 10.3390/ijerph16020250.

[9]

Pasa F, Golkov V, Pfeiffer F, et al. Efficient Deep Network Architectures for Fast Chest X-Ray Tuberculosis Screening and Visualization[J/OL]. Sci Rep, 2019, 9(1): 6268[2019-11-12]. https://www.nature.com/articles/s41598-019-42557-4. DOI: 10.1038/s41598−019−42557−4.

[10]

Lure FYM, Jaeger S, Antani S, 等. 自动化显微镜检测和数字化胸片诊断系统在肺结核筛查中的应用[J]. 新发传染病电子杂志, 2017, 2(1): 5-9.
Lure FYM, Jaeger S, Antani S, et al. Application of automated microscope detection and digital chest radiography diagnostic system in tuberculosis screening[J]. Electr J Emerg Infec DisElectr J Emerg Infec Dis, 2017, 2(1): 5-9.

[11]

Kim DH, MacKinnon T. Artificial intelligence in fracture detection: transfer learning from deep convolutional neural networks[J]. Clin RadiolClin Radiol, 2018, 73(5): 439-445. doi: 10.1016/j.crad.2017.11.015

[12]

Cheng CT, Ho TY, Lee TY, et al. Application of a deep learning algorithm for detection and visualization of hip fractures on plain pelvic radiographs[J]. Eur RadiolEur Radiol, 2019, 29(10): 5469-5477. doi: 10.1007/s00330-019-06167-y

[13]

郑家伟, 李金忠, 钟来平, 等. 口腔鳞状细胞癌临床流行病学研究现状[J]. 中国口腔颌面外科杂志, 2007, 5(2): 83-90. doi: 10.3969/j.issn.1672-3244.2007.02.002
Zheng JW, Li JZ, Zhong LP, et al. Clinical epidemiology and risk factors of oral squamous cell carcinoma: An overview[J]. China J Oral Maxillofac SurgChina J Oral Maxillofac Surg, 2007, 5(2): 83-90. doi: 10.3969/j.issn.1672-3244.2007.02.002

[14]

Forghani R, Chatterjee A, Reinhold C, et al. Head and neck squamous cell carcinoma: prediction of cervical lymph node metastasis by dual-energy CT texture analysis with machine learning[J]. Eur RadiolEur Radiol, 2019, 29(11): 6172-6181. doi: 10.1007/s00330-019-06159-y

[15]

Bur AM, Holcomb A, Goodwin S, et al. Machine learning to predict occult nodal metastasis in early oral squamous cell carcinoma[J]. Oral OncolOral Oncol, 2019, 92(5): 20-25. doi: 10.1016/j.oraloncology.2019.03.011

[16]

Ciompi F, Chung K, van Riel SJ, et al. Towards automatic pulmonary nodule management in lung cancer screening with deep learning[J/OL]. Sci Rep, 2017, 7: 46479[2019-11-12]. https://www.nature.com/articles/srep46479. DOI: 10.1038/srep46479.

[17]

Muehlematter UJ, Mannil M, Becker AS, et al. Vertebral body insufficiency fractures: detection of vertebrae at risk on standard CT images using texture analysis and machine learning[J]. Eur RadiolEur Radiol, 2019, 29(5): 2207-2217. doi: 10.1007/s00330-018-5846-8

[18]

Tomita N, Cheung YY, Hassanpour S. Deep neural networks for automatic detection of osteoporotic vertebral fractures on CT scans[J]. Comput Biol MedComput Biol Med, 2018, 98(7): 8-15. doi: 10.1016/j.compbiomed.2018.05.011

[19]

Zeng LL, Xie L, Shen H, et al. Differentiating patients with Parkinson's Disease from normal controls using gray matter in the cerebellum[J]. CerebellumCerebellum, 2017, 16(1): 151-157. doi: 10.1007/s12311-016-0781-1

[20]

Shinde S, Prasad S, Saboo Y, et al. Predictive markers for Parkinson's disease using deep neural nets on neuromelanin sensitive MRI[J/OL]. Neuroimage Clin, 2019, 22: 101748[2019-11-12]. https://www.sciencedirect.com/science/article/pii/S2213158219300981?via%3Dihub. DOI: 10.1016/j.nicl.2019.101748.

[21]

Li QL, Xu YZ, Chen ZW, et al. Tumor Segmentation in Contrast-Enhanced Magnetic Resonance Imaging for Nasopharyngeal Carcinoma: Deep Learning with Convolutional Neural Network[J/OL]. Biomed Res Int, 2018, 2018: 9128527 [2019-11-12]. http://downloads.hindawi.com/journals/bmri/2018/9128527.pdf. DOI: 10.1155/2018/9128527.

[22]

Lin L, Dou Q, Jin YM, et al. Deep Learning for Automated Contouring of Primary Tumor Volumes by MRI for Nasopharyngeal Carcinoma[J]. RadiologyRadiology, 2019, 291(3): 677-686. doi: 10.1148/radiol.2019182012

[23]

Trebeschi S, van Griethuysen JJM, Lambregts DMJ, et al. Deep Learning for Fully-Automated Localization and Segmentation of Rectal Cancer on Multiparametric MR[J/OL]. Sci Rep, 2017, 7: 5301[2019-11-12]. https://www.nature.com/articles/s41598-017-05728-9. DOI: 10.1038/s41598−017−05728−9.

[24]

Wang JZ, Lu JY, Qin G, et al. Technical Note: A deep learning-based autosegmentation of rectal tumors in MR images[J]. Med PhysMed Phys, 2018, 45(6): 2560-2564. doi: 10.1002/mp.12918

[25]

Ding L, Liu GW, Zhao BC, et al. Artificial intelligence system of faster region-based convolutional neural network surpassing senior radiologists in evaluation of metastatic lymph nodes of rectal cancer[J]. Chin Med JChin Med J, 2019, 132(4): 379-387. doi: 10.1097/CM9.0000000000000095

[26]

Al-Antari MA, Al-Masni MA, Park SU, et al. An Automatic Computer-Aided Diagnosis System for Breast Cancer in Digital Mammograms via Deep Belief Network[J]. J Med Biol EngJ Med Biol Eng, 2018, 38(3): 443-456. doi: 10.1007/s40846-017-0321-6

[27]

Ha R, Mutasa S, Karcich J, et al. Predicting Breast Cancer Molecular Subtype with MRI Dataset Utilizing Convolutional Neural Network Algorithm[J]. J Digit ImagingJ Digit Imaging, 2019, 32(2): 276-282. doi: 10.1007/s10278-019-00179-2

[28]

Shen WC, Chen SW, Wu KC, et al. Prediction of local relapse and distant metastasis in patients with definitive chemoradiotherapy-treated cervical cancer by deep learning from [18F]-fluorodeoxyglucose positron emission tomography/ computed tomography[J]. Eur RadiolEur Radiol, 2019, 29(12): 6741-6749. doi: 10.1007/s00330-019-06265-x

[29]

Shibutani T, Nakajima K, Wakabayashi H, et al. Accuracy of an artificial neural network for detecting a regional abnormality in myocardial perfusion SPECT[J]. Ann Nucl MedAnn Nucl Med, 2019, 33(2): 86-92. doi: 10.1007/s12149-018-1306-4

[30]

Ma LY, Ma CK, Liu YJ, et al. Thyroid Diagnosis from SPECT Images Using Convolutional Neural Network with Optimization [J/OL]. Comput Intell Neurosci, 2019, 2019: 6212759[2019-11-12]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6350547/. DOI: 10.1155/2019/6212759.

[1]	金征宇. 人工智能医学影像应用: 现实与挑战[J]. 放射学实践放射学实践, 2018, 33(10): 989-991. doi: 10.13609/j.cnki.1000-0313.2018.10.001 Jin ZY. AI medical imaging applications: reality and challenges[J]. Radiol PractRadiol Pract, 2018, 33(10): 989-991. doi: 10.13609/j.cnki.1000-0313.2018.10.001
[2]	梁长虹, 刘再毅. 人工智能与医学影像再思考[J]. 中华医学信息导报中华医学信息导报, 2017, 32(22): 21-. doi: 10.3969/j.issn.1000-8039.2017.22.023 Liang CH, Liu ZY. Rethinking artificial intelligence and medical imaging[J]. China Med NewsChina Med News, 2017, 32(22): 21-. doi: 10.3969/j.issn.1000-8039.2017.22.023
[3]	沈旭东. 基于深度学习的时间序列算法综述[J]. 信息技术与信息化信息技术与信息化, 2019, 226(1): 71-76. doi: 10.3969/j.issn.1672-9528.2019.01.021 Shen XD. Survey of time series algorithms based on deep learning[J]. Infor Technol InformatizationInfor Technol Informatization, 2019, 226(1): 71-76. doi: 10.3969/j.issn.1672-9528.2019.01.021
[4]	陈真诚, 倪利莉, 王红艳, 等. 人工智能技术在医学影像专家系统中的应用及发展[J]. 国外医学·生物医学工程分册, 2001, 24(5): 201−206. DOI: 10.3760/cma.j.issn.1673−4181.2001.05.003. Chen ZC, Ni LL, Wang HY, et al. Application and development of artificial intelligence technology in medical imaging expert system[J]. Foreign Med Sci (Biomed Eng Fasc), 2001, 24(5): 201−206. DOI: 10.3760/cma.j.issn.1673−4181.2001.05.003.
[5]	高歌, 马帅, 王霄英. 计算机辅助诊断在医学影像诊断中的基本原理和应用进展[J]. 放射学实践放射学实践, 2016, 31(12): 1127-1129. doi: 10.13609/j.cnki.1000-0313.2016.12.004 Gao G, Ma S, Wang XY. Basic principles and application progress of computer-aided diagnosis in medical imaging diagnosis[J]. Radiol PractRadiol Pract, 2016, 31(12): 1127-1129. doi: 10.13609/j.cnki.1000-0313.2016.12.004
[6]	国务院. 国务院关于印发新一代人工智能发展规划的通知(国发(2017)35号)[EB/OL]. [2019-11-12]. http://www.gov.cn/zhengce/content/2017-07/20/content_5211996.htm. State Council. Notice of the State Council on Printing and Distributing the New Generation Artificial Intelligence Development Plan (Guofa (2017) No. 35)[EB/OL]. [2019-11-12]. http://www.gov.cn/zhengce/content/2017-07/20/content_5211996.htm.
[7]	Yang W, Chen YY, Liu YB, et al. Cascade of multi-scale convolutional neural networks for bone suppression of chest radiographs in gradient domain[J]. Med Image AnalMed Image Anal, 2017, 35(1): 421-433. doi: 10.1016/j.media.2016.08.004
[8]	Heo SJ, Kim Y, Yun S, et al. Deep learning algorithms with demographic information help to detect tuberculosis in chest radiographs in annual workers' health examination data[J/OL]. Int J Environ Res Public Health, 2019, 16(2): e250[2019-11-12]. https://www.mdpi.com/1660-4601/16/2/250. DOI: 10.3390/ijerph16020250.
[9]	Pasa F, Golkov V, Pfeiffer F, et al. Efficient Deep Network Architectures for Fast Chest X-Ray Tuberculosis Screening and Visualization[J/OL]. Sci Rep, 2019, 9(1): 6268[2019-11-12]. https://www.nature.com/articles/s41598-019-42557-4. DOI: 10.1038/s41598−019−42557−4.
[10]	Lure FYM, Jaeger S, Antani S, 等. 自动化显微镜检测和数字化胸片诊断系统在肺结核筛查中的应用[J]. 新发传染病电子杂志新发传染病电子杂志, 2017, 2(1): 5-9. Lure FYM, Jaeger S, Antani S, et al. Application of automated microscope detection and digital chest radiography diagnostic system in tuberculosis screening[J]. Electr J Emerg Infec DisElectr J Emerg Infec Dis, 2017, 2(1): 5-9.
[11]	Kim DH, MacKinnon T. Artificial intelligence in fracture detection: transfer learning from deep convolutional neural networks[J]. Clin RadiolClin Radiol, 2018, 73(5): 439-445. doi: 10.1016/j.crad.2017.11.015
[12]	Cheng CT, Ho TY, Lee TY, et al. Application of a deep learning algorithm for detection and visualization of hip fractures on plain pelvic radiographs[J]. Eur RadiolEur Radiol, 2019, 29(10): 5469-5477. doi: 10.1007/s00330-019-06167-y
[13]	郑家伟, 李金忠, 钟来平, 等. 口腔鳞状细胞癌临床流行病学研究现状[J]. 中国口腔颌面外科杂志中国口腔颌面外科杂志, 2007, 5(2): 83-90. doi: 10.3969/j.issn.1672-3244.2007.02.002 Zheng JW, Li JZ, Zhong LP, et al. Clinical epidemiology and risk factors of oral squamous cell carcinoma: An overview[J]. China J Oral Maxillofac SurgChina J Oral Maxillofac Surg, 2007, 5(2): 83-90. doi: 10.3969/j.issn.1672-3244.2007.02.002
[14]	Forghani R, Chatterjee A, Reinhold C, et al. Head and neck squamous cell carcinoma: prediction of cervical lymph node metastasis by dual-energy CT texture analysis with machine learning[J]. Eur RadiolEur Radiol, 2019, 29(11): 6172-6181. doi: 10.1007/s00330-019-06159-y
[15]	Bur AM, Holcomb A, Goodwin S, et al. Machine learning to predict occult nodal metastasis in early oral squamous cell carcinoma[J]. Oral OncolOral Oncol, 2019, 92(5): 20-25. doi: 10.1016/j.oraloncology.2019.03.011
[16]	Ciompi F, Chung K, van Riel SJ, et al. Towards automatic pulmonary nodule management in lung cancer screening with deep learning[J/OL]. Sci Rep, 2017, 7: 46479[2019-11-12]. https://www.nature.com/articles/srep46479. DOI: 10.1038/srep46479.
[17]	Muehlematter UJ, Mannil M, Becker AS, et al. Vertebral body insufficiency fractures: detection of vertebrae at risk on standard CT images using texture analysis and machine learning[J]. Eur RadiolEur Radiol, 2019, 29(5): 2207-2217. doi: 10.1007/s00330-018-5846-8
[18]	Tomita N, Cheung YY, Hassanpour S. Deep neural networks for automatic detection of osteoporotic vertebral fractures on CT scans[J]. Comput Biol MedComput Biol Med, 2018, 98(7): 8-15. doi: 10.1016/j.compbiomed.2018.05.011
[19]	Zeng LL, Xie L, Shen H, et al. Differentiating patients with Parkinson's Disease from normal controls using gray matter in the cerebellum[J]. CerebellumCerebellum, 2017, 16(1): 151-157. doi: 10.1007/s12311-016-0781-1
[20]	Shinde S, Prasad S, Saboo Y, et al. Predictive markers for Parkinson's disease using deep neural nets on neuromelanin sensitive MRI[J/OL]. Neuroimage Clin, 2019, 22: 101748[2019-11-12]. https://www.sciencedirect.com/science/article/pii/S2213158219300981?via%3Dihub. DOI: 10.1016/j.nicl.2019.101748.
[21]	Li QL, Xu YZ, Chen ZW, et al. Tumor Segmentation in Contrast-Enhanced Magnetic Resonance Imaging for Nasopharyngeal Carcinoma: Deep Learning with Convolutional Neural Network[J/OL]. Biomed Res Int, 2018, 2018: 9128527 [2019-11-12]. http://downloads.hindawi.com/journals/bmri/2018/9128527.pdf. DOI: 10.1155/2018/9128527.
[22]	Lin L, Dou Q, Jin YM, et al. Deep Learning for Automated Contouring of Primary Tumor Volumes by MRI for Nasopharyngeal Carcinoma[J]. RadiologyRadiology, 2019, 291(3): 677-686. doi: 10.1148/radiol.2019182012
[23]	Trebeschi S, van Griethuysen JJM, Lambregts DMJ, et al. Deep Learning for Fully-Automated Localization and Segmentation of Rectal Cancer on Multiparametric MR[J/OL]. Sci Rep, 2017, 7: 5301[2019-11-12]. https://www.nature.com/articles/s41598-017-05728-9. DOI: 10.1038/s41598−017−05728−9.
[24]	Wang JZ, Lu JY, Qin G, et al. Technical Note: A deep learning-based autosegmentation of rectal tumors in MR images[J]. Med PhysMed Phys, 2018, 45(6): 2560-2564. doi: 10.1002/mp.12918
[25]	Ding L, Liu GW, Zhao BC, et al. Artificial intelligence system of faster region-based convolutional neural network surpassing senior radiologists in evaluation of metastatic lymph nodes of rectal cancer[J]. Chin Med JChin Med J, 2019, 132(4): 379-387. doi: 10.1097/CM9.0000000000000095
[26]	Al-Antari MA, Al-Masni MA, Park SU, et al. An Automatic Computer-Aided Diagnosis System for Breast Cancer in Digital Mammograms via Deep Belief Network[J]. J Med Biol EngJ Med Biol Eng, 2018, 38(3): 443-456. doi: 10.1007/s40846-017-0321-6
[27]	Ha R, Mutasa S, Karcich J, et al. Predicting Breast Cancer Molecular Subtype with MRI Dataset Utilizing Convolutional Neural Network Algorithm[J]. J Digit ImagingJ Digit Imaging, 2019, 32(2): 276-282. doi: 10.1007/s10278-019-00179-2
[28]	Shen WC, Chen SW, Wu KC, et al. Prediction of local relapse and distant metastasis in patients with definitive chemoradiotherapy-treated cervical cancer by deep learning from [18F]-fluorodeoxyglucose positron emission tomography/ computed tomography[J]. Eur RadiolEur Radiol, 2019, 29(12): 6741-6749. doi: 10.1007/s00330-019-06265-x
[29]	Shibutani T, Nakajima K, Wakabayashi H, et al. Accuracy of an artificial neural network for detecting a regional abnormality in myocardial perfusion SPECT[J]. Ann Nucl MedAnn Nucl Med, 2019, 33(2): 86-92. doi: 10.1007/s12149-018-1306-4
[30]	Ma LY, Ma CK, Liu YJ, et al. Thyroid Diagnosis from SPECT Images Using Convolutional Neural Network with Optimization [J/OL]. Comput Intell Neurosci, 2019, 2019: 6212759[2019-11-12]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6350547/. DOI: 10.1155/2019/6212759.