Journal of Shandong University (Health Sciences) ›› 2023, Vol. 61 ›› Issue (12): 7-12, 20.doi: 10.6040/j.issn.1671-7554.0.2023.0705
• The innovation and challenge of artificial intelligence in medical imaging—Expert Overview • Previous Articles Next Articles
CLC Number:
| 1 | Russell SJ , Norvig P . Artificial Intelligence, A Modern Approach - 2nd Edition[M]. Upper Saddle River, New Jersey: Prentice Hall, 2003. |
| 2 |
Chen X , Wang X , Zhang K , et al. Recent advances and clinical applications of deep learning in medical image analysis[J]. Med Image Anal, 2022, 79, 102444.
doi: 10.1016/j.media.2022.102444 |
| 3 |
Shokraei Fard A , Reutens DC , Vegh V . From CNNs to GANs for cross-modality medical image estimation[J]. Comput Biol Med, 2022, 146, 105556.
doi: 10.1016/j.compbiomed.2022.105556 |
| 4 |
Bhuva AN , Bai W , Lau C , et al. A Multicenter, scan-rescan, human and machine learning cmr study to test generalizability and precision in imaging biomarker analysis[J]. Circ Cardiovasc Imaging, 2019, 12 (10): e009214.
doi: 10.1161/circimaging.119.009214 |
| 5 |
Aggarwal R , Sounderajah V , Martin G , et al. Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis[J]. NPJ Digit Med, 2021, 4 (1): 65.
doi: 10.1038/s41746-021-00438-z |
| 6 |
Johnson KW , Torres Soto J , Glicksberg BS , et al. Artificial intelligence in cardiology[J]. J Am Coll Cardiol, 2018, 71 (23): 2668- 2679.
doi: 10.1016/j.jacc.2018.03.521 |
| 7 |
Jain S , Indora S , Atal DK . Lung nodule segmentation using Salp Shuffled Shepherd Optimization Algorithm-based Generative Adversarial Network[J]. Comput Biol Med, 2021, 137, 104811.
doi: 10.1016/j.compbiomed.2021.104811 |
| 8 |
Zhao X , Wu Y , Song G , et al. A deep learning model integrating FCNNs and CRFs for brain tumor segmentation[J]. Med Image Anal, 2018, 43, 98- 111.
doi: 10.1016/j.media.2017.10.002 |
| 9 |
Alizadehsani R , Hosseini MJ , Khosravi A , et al. Non-invasive detection of coronary artery disease in high-risk patients based on the stenosis prediction of separate coronary arteries[J]. Comput Methods Programs Biomed, 2018, 162, 119- 127.
doi: 10.1016/j.cmpb.2018.05.009 |
| 10 |
Rajendra Acharya U , Meiburger KM , Wei Koh JE , et al. Automated plaque classification using computed tomography angiography and Gabor transformations[J]. Artif Intell Med, 2019, 100, 101724.
doi: 10.1016/j.artmed.2019.101724 |
| 11 |
Han D , Kolli KK , Gransar H , et al. Machine learning based risk prediction model for asymptomatic individuals who underwent coronary artery calcium score: Comparison with traditional risk prediction approaches[J]. J Cardiovasc Comput Tomogr, 2020, 14 (2): 168- 176.
doi: 10.1016/j.jcct.2019.09.005 |
| 12 |
Han D , Kolli KK , Al'Aref SJ , et al. Machine learning framework to identify individuals at risk of rapid progression of coronary atherosclerosis: From the PARADIGM registry[J]. J Am Heart Assoc, 2020, 9 (5): e013958.
doi: 10.1161/jaha.119.013958 |
| 13 |
Pham DL , Xu C , Prince JL . Current methods in medical image segmentation[J]. Annu Rev Biomed Eng, 2000, 2, 315- 337.
doi: 10.1146/annurev.bioeng.2.1.315 |
| 14 | Ronneberger O, Fischer P, Brox T. U-Net: Convolutional networks for biomedical image segmentation[C]. MICCAI 2015, 2015: 234-241. doi: 10.1007/978-3-319-24574-4_28. |
| 15 |
Shaukat Z , Farooq QUA , Tu S , et al. A state-of-the-art technique to perform cloud-based semantic segmentation using deep learning 3D U-Net architecture[J]. BMC Bioinformatics, 2022, 23 (1): 251.
doi: 10.1186/s12859-022-04794-9 |
| 16 |
Li D , Chu X , Cui Y , et al. Improved U-Net based on contour prediction for efficient segmentation of rectal cancer[J]. Comput Methods Programs Biomed, 2022, 213, 106493.
doi: 10.1016/j.cmpb.2021.106493 |
| 17 |
Li C , Song X , Zhao H , et al. An 8-layer residual U-Net with deep supervision for segmentation of the left ventricle in cardiac CT angiography[J]. Comput Methods Programs Biomed, 2021, 200, 105876.
doi: 10.1016/j.cmpb.2020.105876 |
| 18 |
Lian L , Luo X , Pan C , et al. Lung image segmentation based on DRD U-Net and combined WGAN with Deep Neural Network[J]. Comput Methods Programs Biomed, 2022, 226, 107097.
doi: 10.1016/j.cmpb.2022.107097 |
| 19 |
Jiang L , Ou J , Liu R , et al. RMAU-Net: Residual multi-scale attention U-Net for liver and tumor segmentation in CT images[J]. Comput Biol Med, 2023, 158, 106838.
doi: 10.1016/j.compbiomed.2023.106838 |
| 20 |
Zhou Z , Siddiquee MMR , Tajbakhsh N , et al. UNet++: a nested U-Net architecture for medical image segmentation[J]. Deep Learn Med Image Anal Multimodal Learn Clin Decis Support (2018), 2018, 11045, 3- 11.
doi: 10.1007/978-3-030-00889-5_1 |
| 21 |
Xu Y , Hou S , Wang X , et al. A medical image segmentation method based on improved UNet 3+ network[J]. Diagnostics (Basel), 2023, 13 (3): 576.
doi: 10.3390/diagnostics13030576 |
| 22 |
Isensee F , Jaeger PF , Kohl SAA , et al. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation[J]. Nat Methods, 2021, 18 (2): 203- 211.
doi: 10.1038/s41592-020-01008-z |
| 23 |
Malhotra P , Gupta S , Koundal D , et al. Deep neural networks for medical image segmentation[J]. J Healthc Eng, 2022, 2022, 9580991.
doi: 10.1155/2022/9580991 |
| 24 |
van Ginneken B , Schaefer-Prokop CM , Prokop M . Computer-aided diagnosis: how to move from the laboratory to the clinic[J]. Radiology, 2011, 261 (3): 719- 732.
doi: 10.1148/radiol.11091710 |
| 25 |
Khav N , Ihdayhid AR , Ko B . CT-Derived Fractional Flow Reserve (CT-FFR) in the evaluation of coronary artery disease[J]. Heart Lung Circ, 2020, 29 (11): 1621- 1632.
doi: 10.1016/j.hlc.2020.05.099 |
| 26 | Balasubramanian PK , Lai WC , Seng GH , et al. APESTNet with mask R-CNN for liver tumor segmentation and classification[J]. Cancers (Basel), 2023, 15 (2): 33. |
| 27 |
Wang X , Li BB . Deep learning in head and neck tumor multiomics diagnosis and analysis: review of the literature[J]. Front Genet, 2021, 12, 624820.
doi: 10.3389/fgene.2021.624820 |
| 28 |
Turhan G , Küçük H , Isik EO . Spatio-temporal convolution for classification of alzheimer disease and mild cognitive impairment[J]. Comput Methods Programs Biomed, 2022, 221, 106825.
doi: 10.1016/j.cmpb.2022.106825 |
| 29 |
Sahin ME , Ulutas H , Yuce E , et al. Detection and classification of COVID-19 by using faster R-CNN and mask R-CNN on CT images[J]. Neural Comput Appl, 2023, 35 (18): 13597- 13611.
doi: 10.1007/s00521-023-08450-y |
| 30 |
Eisenberg E , McElhinney PA , Commandeur F , et al. Deep learning-based quantification of epicardial adipose tissue volume and attenuation predicts major adverse cardiovascular events in asymptomatic subjects[J]. Circ Cardiovasc Imaging, 2020, 13 (2): e009829.
doi: 10.1161/circimaging.119.009829 |
| 31 |
van der Voort SR , Incekara F , Wijnenga MMJ , et al. Combined molecular subtyping, grading, and segmentation of glioma using multi-task deep learning[J]. Neuro Oncol, 2023, 25 (2): 279- 289.
doi: 10.1093/neuonc/noac166 |
| 32 |
Ouyang D , He B , Ghorbani A , et al. Video-based AI for beat-to-beat assessment of cardiac function[J]. Nature, 2020, 580 (7802): 252- 256.
doi: 10.1038/s41586-020-2145-8 |
| 33 |
Ruijsink B , Puyol-Antón E , Oksuz I , et al. Fully automated, quality-controlled cardiac analysis from CMR: validation and large-scale application to characterize cardiac function[J]. JACC Cardiovasc Imaging, 2020, 13 (3): 684- 695.
doi: 10.1016/j.jcmg.2019.05.030 |
| 34 | Knott KD , Seraphim A , Augusto JB , et al. The prognostic significance of quantitative myocardial perfusion: an artificial intelligence-based approach using perfusion mapping[J]. Circulation, 2020, 141 (16): 1282- 1291. |
| 35 |
Aerts HJ , Velazquez ER , Leijenaar RT , et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach[J]. Nat Commun, 2014, 5, 4006.
doi: 10.1038/ncomms5006 |
| 36 |
Baessler B , Luecke C , Lurz J , et al. Cardiac MRI and texture analysis of myocardial T1 and T2 maps in myocarditis with acute versus chronic symptoms of heart failure[J]. Radiology, 2019, 292 (3): 608- 617.
doi: 10.1148/radiol.2019190101 |
| 37 |
Di Noto T , von Spiczak J , Mannil M , et al. Radiomics for distinguishing myocardial infarction from myocarditis at late gadolinium enhancement at MRI: comparison with subjective visual analysis[J]. Radiol Cardiothorac Imaging, 2019, 1 (5): e180026.
doi: 10.1148/ryct.2019180026 |
| 38 |
Sun Q , Chen Y , Liang C , et al. Biologic pathways underlying prognostic radiomics phenotypes from paired MRI and RNA sequencing in glioblastoma[J]. Radiology, 2021, 301 (3): 654- 663.
doi: 10.1148/radiol.2021203281 |
| 39 |
Kickingereder P , Neuberger U , Bonekamp D , et al. Radiomic subtyping improves disease stratification beyond key molecular, clinical, and standard imaging characteristics in patients with glioblastoma[J]. Neuro Oncol, 2018, 20 (6): 848- 857.
doi: 10.1093/neuonc/nox188 |
| 40 |
Xi YB , Guo F , Xu ZL , et al. Radiomics signature: A potential biomarker for the prediction of MGMT promoter methylation in glioblastoma[J]. J Magn Reson Imaging, 2018, 47 (5): 1380- 1387.
doi: 10.1002/jmri.25860 |
| 41 |
Wang X , Xie T , Luo J , et al. Radiomics predicts the prognosis of patients with locally advanced breast cancer by reflecting the heterogeneity of tumor cells and the tumor microenvironment[J]. Breast Cancer Res, 2022, 24 (1): 20.
doi: 10.1186/s13058-022-01516-0 |
| 42 | Honeycutt CE , Plotnick R . Image analysis techniques and gray-level co-occurrence matrices (GLCM) for calculating bioturbation indices and characterizing biogenic sedimentary structures[J]. Computers & Geosciences, 2008, 34 (11): 1461- 1472. |
| 43 | 徐子良, 郑敏文. 人工智能在心血管影像的应用现状与展望[J]. 中华放射学杂志, 2021, 55 (6): 5. |
| 44 | Fukushima K , Miyake S . Neocognitron: A self-organizing neural network model for a mechanism of visual pattern recognition, Competition and cooperation in neural nets[M]. Berlin, Heidelberg: Springer, 1982. |
| 45 |
van der Velden BHM , Kuijf HJ , Gilhuijs KGA , et al. Explainable artificial intelligence (XAI) in deep learning-based medical image analysis[J]. Med Image Anal, 2022, 79, 102470.
doi: 10.1016/j.media.2022.102470 |
| 46 |
Diprose WK , Buist N , Hua N , et al. Physician understanding, explainability, and trust in a hypothetical machine learning risk calculator[J]. J Am Med Inform Assoc, 2020, 27 (4): 592- 600.
doi: 10.1093/jamia/ocz229 |
| 47 |
Alkhodari M , Jelinek HF , Karlas A H , et al. Deep learning predicts heart failure with preserved, mid-range, and reduced left ventricular ejection fraction from patient clinical profiles[J]. Front Cardiovasc Med, 2021, 8, 755968.
doi: 10.3389/fcvm.2021.755968 |
| [1] | LIANG Chen, YAO Lijuan, LYU Longfei, TANG Ze, QIN Da, CUI Youbin,YU Xiaoqi. Application of 3D reconstruction combined with CT-guided puncture localization in minimally invasive treatment of pulmonary ground-glass nodules [J]. Journal of Shandong University (Health Sciences), 2026, 64(5): 74-82. |
| [2] | Intelligent Orthopedics Subgroup of Chinese Association of Orthopedic, Subgroup for Prevention and Control of Spinal and Spinal Cord Injury Diseases of Professional Committee for Prevention and Control of Spinal Diseases of Chinese Preventive Medicine Association. Expert consensus on measurement sites and annotation of artificial intelligence-based spinal degenerative imaging(2025) [J]. Journal of Shandong University (Health Sciences), 2026, 64(2): 1-10. |
| [3] | JI Xinyu, YU Siyi, SUN Yuanyuan, JI Bing. Orthopedic disease diagnosis and treatment assistance methods based on artificial intelligence and gait analysis [J]. Journal of Shandong University (Health Sciences), 2026, 64(2): 34-43. |
| [4] | WANG Jianmin, LI Xiaofeng, YOU Zhitao, DONG Shengjie, ZHAO Yuchi, LI Zhanju, ZOU Dexin, ZHANG Jianfeng, SUN Tao, DU Wei. Construction of a chronic post-surgical pain prediction model for posterior lumbar interbody fusion surgery based on interpretable machine learning [J]. Journal of Shandong University (Health Sciences), 2026, 64(2): 78-88. |
| [5] | YANG Fan. Multimodal medical data fusion technology and its application [J]. Journal of Shandong University (Health Sciences), 2025, 63(8): 17-40. |
| [6] | ZHANG Xinru, LI Yang, SUN Meng, NIE Wei, MA Zhe. Application and evaluation of Vision-LSTM model in diagnostic ultrasound imaging of Thyroid Imaging Reporting and Data System Category 4b thyroid nodules [J]. Journal of Shandong University (Health Sciences), 2025, 63(11): 68-74. |
| [7] | WU Qiqi, CHENG Miaomiao, XIAO Xiaoyan. Multimodal models in the field of kidney disease [J]. Journal of Shandong University (Health Sciences), 2025, 63(10): 117-124. |
| [8] | LIANG Bowen, LU Qingsheng. Advances in robotic-assisted endovascular aortic repair [J]. Journal of Shandong University (Health Sciences), 2024, 62(9): 61-65. |
| [9] | TANG Yuning, PAN Tianyue, DONG Zhihui, FU Weiguo. Research progress of deep learning in automatic segmentation of aortic images [J]. Journal of Shandong University (Health Sciences), 2024, 62(9): 66-73. |
| [10] | ZHANG Jinghui, WANG Juan, ZHAO Yujie, DUAN Miao, LIU Yiran, LIN Minjuan, QIAO Xu, LI Zhen, ZUO Xiuli. Construction of a machine learning-based tongue diagnosis model for gastrointestinal diseases [J]. Journal of Shandong University (Health Sciences), 2024, 62(1): 38-47. |
| [11] | Shiqing FENG. Computer vision and lumbar degenerative disease [J]. Journal of Shandong University (Health Sciences), 2023, 61(3): 1-6. |
| [12] | Nan WU,Jianguo ZHANG,Yuanpeng ZHU,Guilin CHEN,Zefu CHEN. Application of artificial intelligence in the diagnosis and treatment of spinal deformity [J]. Journal of Shandong University (Health Sciences), 2023, 61(3): 14-20. |
| [13] | Lin HUANG,Zhen CHE,Ming LI,Yuxi LI,Qing NING. Research advances of artificial intelligence in the diagnosis and treatment of orthopaedic diseases [J]. Journal of Shandong University (Health Sciences), 2023, 61(3): 37-45. |
| [14] | LIU Yajun, YUAN Qiang, WU Jingye, HAN Xiaoguang, LANG Zhao, ZHANG Yong. Preliminary exploration of automatic planning of lumbar pedicle screws based on cone-beam CT in 130 cases [J]. Journal of Shandong University (Health Sciences), 2023, 61(3): 80-89. |
| [15] | WANG Hui, WANG Lianlei, WU Tianchi, TIAN Yonghao, YUAN Suomao, WANG Xia, LYU Weijia, LIU Xinyu. Artificial intelligence-assisted 3D printing of surgical guides for pedicle screw Insertion in scoliosis surgeries [J]. Journal of Shandong University (Health Sciences), 2023, 61(3): 127-133. |
|
||