대한방사선기술학회지:방사선기술과학 (Journal of radiological science and technology)

  • 제43권3호
  • /
  • Pages.195-208
  • /
  • 2020
  • /
  • 2288-3509(pISSN)
  • /
  • 2384-1168(eISSN)
대한방사선과학회 (Korean Society of Radiological Science)
권호 표지
DOI QR코드

DOI QR Code

AI 의료영상 분석의 개요 및 연구 현황에 대한 고찰

Artificial Intelligence Based Medical Imaging: An Overview

  • 홍준용 (동서대학교 융합방사선학과) ;
  • 박상현 (동서대학교 융합방사선학과) ;
  • 정영진 (동서대학교 방사선학과)
  • Hong, Jun-Yong (Department of Multidisciplinary Radiological Science, Dongseo University) ;
  • Park, Sang Hyun (Department of Multidisciplinary Radiological Science, Dongseo University) ;
  • Jung, Young-Jin (Department of Radiology, Dongseo University)
  • 투고 : 2020.05.06
  • 심사 : 2020.06.22
  • 발행 : 2020.06.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Artificial intelligence(AI) is a field of computer science that is defined as allowing computers to imitate human intellectual behavior, even though AI's performance is to imitate humans. It is grafted across software-based fields with the advantages of high accuracy and speed of processing that surpasses humans. Indeed, the AI based technology has become a key technology in the medical field that will lead the development of medical image analysis. Therefore, this article introduces and discusses the concept of deep learning-based medical imaging analysis using the principle of algorithms for convolutional neural network(CNN) and back propagation. The research cases application of the AI based medical imaging analysis is used to classify the various disease(such as chest disease, coronary artery disease, and cerebrovascular disease), and the performance estimation comparing between AI based medical imaging classifier and human experts.

키워드

참고문헌

  1. Harman M. The role of artificial intelligence in software engineering. In: 2012 First International Workshop on Realizing AI Synergies in Software Engineering (RAISE). IEEE. 2012 Jun:1-6.
  2. Pesapane F, Codari M, Sardanelli F. Artificial intelligence in medical imaging: Threat or opportunity? Radiologists again at the forefront of innovation in medicine. Eur Radiol Exp. 2018 Oct;2(1):35. https://doi.org/10.1186/s41747-018-0061-6
  3. Trinder JC, Wang Y, Sowmya A, Palhang M. Artificial intelligence in 3-D feature extraction. In Automatic Extraction of Man-Made Objects from Aerial and Space Images. 2nd ed. Basel: Birkhauser; 1997.
  4. Shen D, Wu G, Suk HI. Deep learning in medical image analysis. Annu Rev Biomed Eng. 2017 Jun;19:221-48. https://doi.org/10.1146/annurev-bioeng-071516-044442
  5. RavR D, Wong C, Deligianni F, Berthelot M, Andreu-Perez J, Lo B, et al. Deep learning for health informatics. IEEE J Biomed Health Inform. 2017 Jan;21(1):4-21. https://doi.org/10.1109/JBHI.2016.2636665
  6. LeCun Y, Boser B, Denker JS, Henderson D. Backpropagation applied to handwritten zip code recognition. Neural Comput. 1989;1(4):541-51. https://doi.org/10.1162/neco.1989.1.4.541
  7. Neyshabur B, Tomioka R, Srebro N. In search of the real inductive bias: On the role of implicit regularization in deep learning. arXiv preprint arXiv: 1412.6614; 2014.
  8. Yu K, Xu W, Gong Y. Deep learning with kernel regularization for visual recognition. Adv Neural Inf Process Syst. 2009:1889-96.
  9. Kukacka J, Golkov V, Cremers D. Regularization for deep learning: A taxonomy. arXiv preprint arXiv:1710.10686; 2017.
  10. Yoshida Y, Miyato T. Spectral norm regularization for improving the generalizability of deep learning. arXiv preprint arXiv:1705.10941; 2017.
  11. Russell S, Norvig P. Artificial intelligence a modern approach. 3rd ed. New Jersey: Prentice Hall; 2009.
  12. Mitchell TM. Machine learning. New York: McGraw-Hill; 1997.
  13. Vial A, Stirling D, Field M, Ros M, Ritz CH, Carolan, MG, et al. The role of deep learning and radiomic feature extraction in cancer-specific predictive modelling: A review. Translational Cancer Res. 2018;7(3):803-16. https://doi.org/10.21037/tcr.2018.05.02
  14. Ang JC, Mirzal A, Haron H, Hamed HNA. Supervised, unsupervised, and semi-supervised feature selection: A review on gene selection. IEEE/ACM Trans Comput Biol Bioinform. 2015;13(5):971-89. https://doi.org/10.1109/TCBB.2015.2478454
  15. Lin YZ, Nie ZH, Ma HW. Structural damage detection with automatic feature-extraction through deep learning. Computer-Aided Civ Inf Eng. 2017;32(12):1025-46. https://doi.org/10.1111/mice.12313
  16. Yang J, Zhao YQ, Chan JCW. Learning and transferring deep joint spectral-spatial features for hyperspectral classification. IEEE Trans Geosci Remote Sens. 2017;55(8):4729-42. https://doi.org/10.1109/TGRS.2017.2698503
  17. Weiss KR, Khoshgoftaar TM. Comparing transfer learning and traditional learning under domain class imbalance. In 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA). 2017 Dec:337-43.
  18. Yoo Y, Brosch T, Traboulsee A, Li DK, Tam R. Deep learning of image features from unlabeled data for multiple sclerosis lesion segmentation. In International Workshop on Machine Learning in Medical Imaging. 2014 Sep:117-24.
  19. Cai Y, Landis M, Laidley DT, Kornecki A, Lum A, Li S. Multi-modal vertebrae recognition using transformed deep convolution network. Comput Med Imaging Graph. 2016 Jul;51:11-9. https://doi.org/10.1016/j.compmedimag.2016.02.002
  20. Jaumard-Hakoun A, Xu K, Roussel-Ragot P, Dreyfus G, Denby, B. Tongue contour extraction from ultrasound images based on deep neural network. arXiv preprint arXiv:1605.05912; 2016.
  21. Saito K. Deep learning starting from the bottom. Seoul: Hanbit Media; 2017.
  22. Kim H, Nam H, Jung W, Lee J. Performance analysis of CNN frameworks for GPUs. In 2017 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS). 2017 Apr:55-64.
  23. Agarap AF. Deep learning using rectified linear units (relu). arXiv preprint arXiv:1803.08375; 2018.
  24. Lee JG, Jun S, Cho YW, Lee H, Kim GB, Seo JB. Deep learning in medical imaging: General overview. Korean J Radiol. 2017 Aug;18(4):570-84. https://doi.org/10.3348/kjr.2017.18.4.570
  25. Wakui Y, Wakui S. Math to understand deep learning. Seoul: Hanbit Media; 2018.
  26. Rajpurkar P, Irvin J, Ball RL, Zhu K, Yang B, Mehta H, et al. Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists. PLoS Medicine. 2018;15(11):e1002686. https://doi.org/10.1371/journal.pmed.1002686
  27. Shadmi R, Mazo V, Bregman-Amitai O, Elnekave E. Fully-convolutional deep-learning based system for coronary calcium score prediction from non-contrast chest CT. In 2018 IEEE 15th International Symposium on Biomedical Imaging. 2018 Apr:24-8.
  28. Menze BH, Jakab A, Bauer S, Kalpathy-Cramer J, Farahani K, Kirby J, et al. The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Transactions on Medical Imaging. 2014;34(10):1993-2024. https://doi.org/10.1109/TMI.2014.2377694
  29. Urban G, Bendszus M, Hamprecht F, Kleesiek J. Multi-modal brain tumor segmentation using deep convolutional neural networks. MICCAI BraTS (brain tumor segmentation) challenge. Proceedings, Winning Contribution. 2014:31-5.
  30. Zikic D, Ioannou Y, Brown M, Criminisi A. Segmentation of brain tumor tissues with convolutional neural networks. Proceedings MICCAI-BRATS. 2014:36-9.
  31. Oman O, Makela T, Salli E, Savolainen S, Kangasniemi M. 3D convolutional neural networks applied to CT angiography in the detection of acute ischemic stroke. Eur Radiol Exp. 2019;3(1):8. https://doi.org/10.1186/s41747-019-0085-6
  32. Yang W, Hong JY, Kim JY, Paik SH, Lee SH, Park JS, et al. A novel singular value decomposition-based denoising method in 4-dimensional computed tomography of the brain in stroke patients with statistical evaluation. Sensors. 2020;20:3063. https://doi.org/10.3390/s20113063
  33. Park HY, Pyeon D, Kim DH, Jung Y. Dynamic computed tomography based on spatio-temporal analysis in acute stroke: Preliminary study. J Radiol Sci Technol. 2016;39:543-7. https://doi.org/10.17946/JRST.2016.39.4.08
  34. Kim D, Jung Y. Simulation study for feature identification of dynamic medical image reconstruction technique based on singular value decomposition. J Radiol Sci Technol. 2019;42:119-30. https://doi.org/10.17946/JRST.2019.42.2.119
  35. Liu M, Zhang J, Adeli E, Shen D. Landmark-based deep multi-instance learning for brain disease diagnosis. Med Image Anal. 2018;43:157-68. https://doi.org/10.1016/j.media.2017.10.005
  36. Choi H, Jin KH. Alzheimer's Disease Neuroimaging Initiative. Predicting cognitive decline with deep learning of brain metabolism and amyloid imaging. Behav Brain Res. 2018;344:103-9. https://doi.org/10.1016/j.bbr.2018.02.017
  37. Huynh BQ, Li H, Giger ML. Digital mammographic tumor classification using transfer learning from deep convolutional neural networks. J Med Imaging. 2016;3(3):034501. https://doi.org/10.1117/1.JMI.3.3.034501
  38. McKinney SM, Sieniek M, Godbole V, Godwin J, Antropova N, Ashrafian H, et al. International evaluation of an AI system for breast cancer screening. Nature. 2020;577(7788):89-94. https://doi.org/10.1038/s41586-019-1799-6
  39. Christe A, Peters AA, Drakopoulos D, Heverhagen JT, Geiser T, Stathopoulou T, et al. Computer-aided diagnosis of pulmonary fibrosis using deep learning and CT images. Invest Radiol. 2019;54(10):627. https://doi.org/10.1097/rli.0000000000000574
  40. Hamidinekoo A, Denton E, Rampun A, Honnor K, Zwiggelaar R. Deep learning in mammography and breast histology, an overview and future trends. Med Image Anal. 2018;47:45-67. https://doi.org/10.1016/j.media.2018.03.006
  41. Cheng JZ, Ni D, Chou YH, Qin J, Tiu CM, Chang YC. Computer-aided diagnosis with deep learning architecture: applications to breast lesions in US images and pulmonary nodules in CT scans. Sci Rep. 2016;6(1):1-13. https://doi.org/10.1038/s41598-016-0001-8
  42. Hua KL, Hsu CH, Hidayati SC, Cheng WH, Chen YJ. Computer-aided classification of lung nodules on computed tomography images via deep learning technique. Onco Targets Ther. 2015;8.
  43. Dross PE, Sumner R, Tysowsky M, Aujero MP. International radiology for developing countries: The delivery of medical imaging services to a Honduran radiologic scarce zone. J Am Coll Radiol. 2014;11(12):1173-7. https://doi.org/10.1016/j.jacr.2014.09.022
  44. Kwon DI. Only about 10 people in the emergency room nationwide who specialize in imaging medicine to read CT.MRI. Hankook Ilbo, 2017.11.20.
  45. Wang X, Peng Y, Lu L, Lu Z, Bagheri M, Summers RM. Chest X-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017:2097-106.
  46. Jack Jr CR, Bernstein MA, Fox NC, Thompson P, Alexander G, Harvey D. The Alzheimer's disease neuroimaging initiative (ADNI): MRI methods. J Magn Reson Imaging. 2008;27(4):685-91. https://doi.org/10.1002/jmri.21049
  47. Malone IB, Cash D, Ridgway GR, MacManus DG, Ourselin S, Fox NC, et al. MIRIAD-Public release of a multiple time point Alzheimer's MR imaging dataset. NeuroImage. 2013;70:33-6. https://doi.org/10.1016/j.neuroimage.2012.12.044
  48. Moreira IC, Amaral I, Domingues I, Cardoso A, Cardoso MJ, Cardoso JS. Inbreast: Toward a full-field digital mammographic database. Acad Radiol. 2012;19(2):236-48. https://doi.org/10.1016/j.acra.2011.09.014
  49. Razzak MI, Naz S, Zaib A. Deep learning for medical image processing: Overview, challenges and the future. In Classification in BioApps. 2018:323-50.
  50. Ding Q, Chen G, Zhang X, Huang Q, Ji H, Gao H. Low-dose CT with deep learning regularization via proximal forward backward splitting. Phys Med. 2020.
  51. Shin YJ, Chang W, Ye JC, Kang E, Oh DY, Lee YJ. Low-dose abdominal CT using a deep learning-based denoising algorithm: A comparison with CT reconstructed with filtered back projection or iterative reconstruction algorithm. Korean J Radiol. 2020;21(3):356-64. https://doi.org/10.3348/kjr.2019.0413
  52. Xie D, Li Y, Yang H, Bai L, Wang T, Zhou F. Denoising arterial spin labeling perfusion MRI with deep machine learning. Magn Reson Imaging. 2020.
  53. Chen KT, Gong E, de Carvalho Macruz FB, Xu J, Boumis A, Khalighi M. Ultra-low-dose 18F-florbetaben amyloid PET imaging using deep learning with multi-contrast MRI inputs. Radiology. 2019; 290(3):649-56. https://doi.org/10.1148/radiol.2018180940
  54. Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S. Generative adversarial nets. Adv Neural Inf Processing Systems. 2014:2672-80.
  55. Wolterink JM, Dinkla AM, Savenije MH, Seevinck PR, van den Berg CA, Isgum I. Deep MR to CT synthesis using unpaired data. In International Workshop on Simulation and Synthesis in Medical Imaging. 2017 Sep:14-23.
  56. Yi X, Babyn P. Sharpness-aware low-dose CT denoising using conditional generative adversarial network. J Digit Imaging. 2018;31(5):655-69. https://doi.org/10.1007/s10278-018-0056-0
  57. SRnchez I, Vilaplana V. Brain MRI super-resolution using 3D generative adversarial networks. arXiv preprint arXiv:1812.11440; 2018.
  58. Ran M, Hu J, Chen Y, Chen H, Sun H, Zhou J. Denoising of 3D magnetic resonance images using a residual encoder-decoder Wasserstein generative adversarial network. Medl Image Anal. 2019; 55:165-80. https://doi.org/10.1016/j.media.2019.05.001
  59. Nie D, Trullo R, Lian J, Petitjean C, Ruan S, Wang Q. Medical image synthesis with context-aware generative adversarial networks. In International Conference on Medical Image Computing and Computer-assisted Intervention. 2017 Sep:417-25.