Progress in Medical Physics (한국의학물리학회지:의학물리)

Korean Society of Medical Physics (한국의학물리학회)
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Medical Image Analysis Using Artificial Intelligence

  • Yoon, Hyun Jin (Department of Nuclear Medicine, Dong-A University Medical Center, Dong-A University College of Medicine) ;
  • Jeong, Young Jin (Department of Nuclear Medicine, Dong-A University Medical Center, Dong-A University College of Medicine) ;
  • Kang, Hyun (Institute of Convergence Bio-Health, Dong-A University) ;
  • Jeong, Ji Eun (Department of Nuclear Medicine, Dong-A University Medical Center, Dong-A University College of Medicine) ;
  • Kang, Do-Young (Department of Nuclear Medicine, Dong-A University Medical Center, Dong-A University College of Medicine)
  • Received : 2019.05.02
  • Accepted : 2019.05.21
  • Published : 2019.06.30
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Abstract

Purpose: Automated analytical systems have begun to emerge as a database system that enables the scanning of medical images to be performed on computers and the construction of big data. Deep-learning artificial intelligence (AI) architectures have been developed and applied to medical images, making high-precision diagnosis possible. Materials and Methods: For diagnosis, the medical images need to be labeled and standardized. After pre-processing the data and entering them into the deep-learning architecture, the final diagnosis results can be obtained quickly and accurately. To solve the problem of overfitting because of an insufficient amount of labeled data, data augmentation is performed through rotation, using left and right flips to artificially increase the amount of data. Because various deep-learning architectures have been developed and publicized over the past few years, the results of the diagnosis can be obtained by entering a medical image. Results: Classification and regression are performed by a supervised machine-learning method and clustering and generation are performed by an unsupervised machine-learning method. When the convolutional neural network (CNN) method is applied to the deep-learning layer, feature extraction can be used to classify diseases very efficiently and thus to diagnose various diseases. Conclusions: AI, using a deep-learning architecture, has expertise in medical image analysis of the nerves, retina, lungs, digital pathology, breast, heart, abdomen, and musculo-skeletal system.

Keywords

References

  1. Farooq A, Anwar S, Awais M, Alnowami M. Artificial intelligence based smart diagnosis of alzheimer's disease and mild cognitive impairment. 2017;1-4.
  2. Vieira S, Pinaya WHL, Mechelli A. Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: Methods and applications. Neuroscience & Biobehavioral Reviews. 2017;74:58-75. https://doi.org/10.1016/j.neubiorev.2017.01.002
  3. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521:436. https://doi.org/10.1038/nature14539
  4. Zeiler MD, Fergus R. Visualizing and understanding convolutional networks. 2014;818-833.
  5. Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542:115. https://doi.org/10.1038/nature21056
  6. Gulshan V, Peng L, Coram M, et al.Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016;316:2402-2410. https://doi.org/10.1001/jama.2016.17216
  7. Golan R, Jacob C, Denzinger J. Lung nodule detection in CT images using deep convolutional neural networks. 2016;243-250.
  8. Kooi T, Litjens G, Van Ginneken B, et al. Large scale deep learning for computer aided detection of mammographic lesions. Med Image Anal. 2017;35:303-312. https://doi.org/10.1016/j.media.2016.07.007
  9. Liu F, Zhou Z, Samsonov A, et al. Deep learning approach for evaluating knee MR images: Achieving high diagnostic performance for cartilage lesion detection. Radiology. 2018;289:160-169. https://doi.org/10.1148/radiol.2018172986
  10. Sharif MS, Abbod M, Amira A, Zaidi H. Artificial neural network-based system for PET volume segmentation. Journal of Biomedical Imaging. 2010:4.
  11. Illan I, Gorriz JM, Ramirez J, et al. 18F-FDG PET imaging analysis for computer aided Alzheimer's diagnosis. Inf Sci. 2011;181:903-916. https://doi.org/10.1016/j.ins.2010.10.027
  12. Bien N, Rajpurkar P, Ball RL, et al. Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet. PLoS Medicine. 2018;15:e1002699. https://doi.org/10.1371/journal.pmed.1002699
  13. Taylor AG, Mielke C, Mongan J. Automated detection of moderate and large pneumothorax on frontal chest X-rays using deep convolutional neural networks: A retrospective study. PLoS Medicine. 2018;15:e1002697. https://doi.org/10.1371/journal.pmed.1002697
  14. Woods RP, Grafton ST, Holmes CJ, Cherry SR, Mazziotta JC. Automated image registration: I. general methods and intrasubject, intramodality validation. J Comput Assist Tomogr. 1998;22:139-152. https://doi.org/10.1097/00004728-199801000-00027
  15. DCMTK V364. Https://support.dcmtk.org/docs/classDicomImage.html#ac1b5118cbae9e797aa55940fcd60258e. 2019.
  16. Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. ArXiv Preprint arXiv:1409.1556 (2014).
  17. Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions. 2015;1-9.
  18. He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. 2016;770-778.
  19. Chollet F. Xception: Deep learning with depthwise separable convolutions. 2017;1251-1258.
  20. Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. 2012;1097-1105.
  21. Salehinejad H, Valaee S, Dowdell T, Colak E, Barfett J. Generalization of deep neural networks for chest pathology classification in x-rays using generative adversarial networks. 2018;990-994.
  22. Turk M, Pentland A. Eigenfaces for recognition. J Cogn Neurosci. 1991;3:71-86. https://doi.org/10.1162/jocn.1991.3.1.71
  23. Korez R, Likar B, Pernus F, Vrtovec T. Model-based segmentation of vertebral bodies from MR images with 3D CNNs. 2016;433-441.
  24. Lopez M, Ramirez J, Gorriz JM, et al. Principal component analysis-based techniques and supervised classification schemes for the early detection of alzheimer's disease. Neurocomputing. 2011;74:1260-1271. https://doi.org/10.1016/j.neucom.2010.06.025
  25. Gorriz J, Lassl A, Ramirez J, Salas-Gonzalez D, Puntonet C, Lang E. Automatic selection of ROIs in functional imaging using gaussian mixture models. Neurosci Lett. 2009;460:108-111. https://doi.org/10.1016/j.neulet.2009.05.039
  26. Suk H, Lee S, Shen D, Alzheimer's Disease Neuroimaging Initiative. Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis. Neuroimage. 2014;101:569-582. https://doi.org/10.1016/j.neuroimage.2014.06.077
  27. Payan A, Montana G. Predicting alzheimer's disease: A neuroimaging study with 3D convolutional neural networks. ArXiv Preprint arXiv:1502.02506 (2015).
  28. Hosseini-Asl E, Gimel'farb G, El-Baz A. Alzheimer's disease diagnostics by a deeply supervised adaptable 3D convolutional network. ArXiv Preprint arXiv:1607.00556 (2016).
  29. de Brebisson A, Montana G. Deep neural networks for anatomical brain segmentation. 2015;20-28.
  30. Choi H, Jin KH. Fast and robust segmentation of the striatum using deep convolutional neural networks. J Neurosci Methods. 2016;274:146-153. https://doi.org/10.1016/j.jneumeth.2016.10.007
  31. Chen H, Dou Q, Yu L, Qin J, Heng P. VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images. Neuroimage. 2018;170:446-455. https://doi.org/10.1016/j.neuroimage.2017.04.041
  32. Nie D, Cao X, Gao Y, Wang L, Shen D. Estimating CT image from MRI data using 3D fully convolutional networks. Anonymous : Springer, (2016), pp. 170-178.
  33. Li R, Zhang W, Suk H, et al. Deep learning based imaging data completion for improved brain disease diagnosis. 2014;305-312.
  34. Kim H, Hwang S. Deconvolutional feature stacking for weakly-supervised semantic segmentation. ArXiv Preprint arXiv:1602.04984 (2016).
  35. Shin H, Roberts K, Lu L, Demner-Fushman D, Yao J, Summers RM. Learning to read chest x-rays: Recurrent neural cascade model for automated image annotation. 2016;2497-2506.
  36. Gao M, Xu Z, Lu L, et al. Segmentation label propagation using deep convolutional neural networks and dense conditional random field. 2016;1265-1268.
  37. Gao M, Bagci U, Lu L, et al. Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization. 2018;6:1-6. https://doi.org/10.1080/1025584031000065956
  38. Rajkomar A, Lingam S, Taylor AG, Blum M, Mongan J. High-throughput classification of radiographs using deep convolutional neural networks. J Digital Imaging. 2017;30:95-101. https://doi.org/10.1007/s10278-016-9914-9
  39. Fonseca P, Mendoza J, Wainer J, et al. Automatic breast density classification using a convolutional neural network architecture search procedure. 2015;9414:941428.
  40. Dalmis MU, Litjens G, Holland K, et al. Using deep learning to segment breast and fibroglandular tissue in MRI volumes. Med Phys. 2017;44:533-546. https://doi.org/10.1002/mp.12079
  41. Qiu Y, Wang Y, Yan S, et al. An initial investigation on developing a new method to predict short-term breast cancer risk based on deep learning technology. 2016;9785:978521.
  42. Avendi M, Kheradvar A, Jafarkhani H. A combined deeplearning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI. Med Image Anal. 2016;30:108-119. https://doi.org/10.1016/j.media.2016.01.005
  43. Oktay O, Bai W, Lee M, et al. Multi-input cardiac image super-resolution using convolutional neural networks. 2016;246-254.
  44. Lessmann N, Isgum I, Setio AA, et al. Deep convolutional neural networks for automatic coronary calcium scoring in a screening study with low-dose chest CT. 2016;9785:978511.
  45. Wolterink JM, Leiner T, de Vos BD, van Hamersvelt RW, Viergever MA, Isgum I. Automatic coronary artery calcium scoring in cardiac CT angiography using paired convolutional neural networks. Med Image Anal. 2016;34:123-136. https://doi.org/10.1016/j.media.2016.04.004
  46. Cai Y, Landis M, Laidley DT, Kornecki A, Lum A, Li S. Multi-modal vertebrae recognition using transformed deep convolution network. Comput Med Imaging Graphics. 2016;51:11-19 https://doi.org/10.1016/j.compmedimag.2016.02.002
  47. Miao S, Wang ZJ, Liao R. A CNN regression approach for real-time 2D/3D registration. IEEE Trans Med Imaging. 2016;35:1352-1363. https://doi.org/10.1109/TMI.2016.2521800
  48. Spampinato C, Palazzo S, Giordano D, Aldinucci M, Leonardi R. Deep learning for automated skeletal bone age assessment in X-ray images. Med Image Anal. 2017;36:41-51. https://doi.org/10.1016/j.media.2016.10.010
  49. Frid-Adar M, Diamant I, Klang E, Amitai M, Goldberger J, Greenspan H. GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification. Neurocomputing. 2018;321:321-331. https://doi.org/10.1016/j.neucom.2018.09.013

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