Prog Med Phys.  2019 Jun;30(2):49-58. 10.14316/pmp.2019.30.2.49.

Medical Image Analysis Using Artificial Intelligence

• Yoon HJ
• Jeong YJ
• Kang H
• Jeong JE
• Kang DY

• KMID: 2451500
• DOI: http://doi.org/10.14316/pmp.2019.30.2.49

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.

Keyword

Artificial Intelligence (AI); Medical images; Deep-learning; Machine-learning; Convolutional Neural Network (CNN); Big data

MeSH Terms

Abdomen
Artificial Intelligence*
Breast
Classification
Diagnosis
Heart
Lung
Methods
Pathology
Retina

Figure

• 

  Fig. 1 Medical image diagnosis method using artificial intelligence (AI).

• 

  Fig. 2 Flow chart of medical imaging AI diagnosis system development.

• 

  Fig. 3 Developed SortDB ver1.0 for DICOM, hdr/img, nii image file viewer, delineation, cropping, and file conversion function. The environment (a) and the functional flowchart (b) of SortDB ver1.0 are displayed. Dicom PET images can be converted to multi-frame nii files, jpg, gif and save, and the preprocessing process for normalization can be performed quickly and conveniently.

Reference

1. Farooq A, Anwar S, Awais M, Alnowami M. Artificial intelligence based smart diagnosis of alzheimer's disease and mild cognitive impairment. 2017. p. 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 and Biobehavioral Reviews. 2017; 74:58–75.

3. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015; 521:436.

4. Zeiler MD, Fergus R. Visualizing and understanding convolutional networks. 2014. p. 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.

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.

7. Golan R, Jacob C, Denzinger J. Lung nodule detection in CT images using deep convolutional neural networks. 2016. p. 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.

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.

10. Sharif MS, Abbod M, Amira A, Zaidi H. Artificial neural network-based system for PET volume segmentation. International Journal of Biomedical Imaging. 2010; 2010:105610.

11. Illán I, Górriz JM, Ramírez J, et al. 18F-FDG PET imaging analysis for computer aided Alzheimer’s diagnosis. Inf Sci. 2011; 181:903–916.

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.

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.

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.

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. p. 1–9.

18. He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. 2016. p. 770–778.

19. Chollet F. Xception: Deep learning with depthwise separable convolutions. 2017. p. 1251–1258.

20. Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. 2012. p. 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. p. 990–994.

22. Turk M, Pentland A. Eigenfaces for recognition. J Cogn Neurosci. 1991; 3:71–86.

23. Korez R, Likar B, Pernuš F, Vrtovec T. Model-based segmentation of vertebral bodies from MR images with 3D CNNs. 2016. p. 433–441.

24. López M, Ramírez J, Górriz 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.

25. Górriz JM, Lassl A, Ramírez 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.

26. Suk H, Lee S, Shen D, Alzheimer’s Disease. Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis. Neuroimage. 2014; 101:569–582.

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. p. 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.

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.

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. p. 170–178.

33. Li R, Zhang W, Suk H, et al. Deep learning based imaging data completion for improved brain disease diagnosis. 2014. p. 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. p. 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. p. 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.

38. Rajkomar A, Lingam S, Taylor AG, Blum M, Mongan J. High-throughput classification of radiographs using deep convolutional neural networks. J Digit Imaging. 2017; 30:95–101.

39. Fonseca P, Mendoza J, Wainer J, et al. Automatic breast density classification using a convolutional neural network architecture search procedure. 2015. 9414:p. 941428.

40. Dalmış 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.

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:p. 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.

43. Oktay O, Bai W, Lee M, et al. Multi-input cardiac image super-resolution using convolutional neural networks. 2016. p. 246–254.

44. Lessmann N, Išgum 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:p. 978511.

45. Wolterink JM, Leiner T, de Vos BD, van Hamersvelt RW, Viergever MA, Išgum I. Automatic coronary artery calcium scoring in cardiac CT angiography using paired convolutional neural networks. Med Image Anal. 2016; 34:123–136.

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 Graph. 2016; 51:11–19.

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.

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.

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.