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Hepatology International

, Volume 13, Issue 4, pp 416–421 | Cite as

Current status and perspectives for computer-aided ultrasonic diagnosis of liver lesions using deep learning technology

  • Naoshi NishidaEmail author
  • Makoto Yamakawa
  • Tsuyoshi Shiina
  • Masatoshi Kudo
Review Article
  • 252 Downloads

Abstract

An ultrasound (US) examination is a common noninvasive technique widely applied for diagnosis of a variety of diseases. Based on the rapid development of US equipment, many US images have been accumulated and are now available and ready for the preparation of a database for the development of computer-aided US diagnosis with deep learning technology. On the contrary, because of the unique characteristics of the US image, there could be some issues that need to be resolved for the establishment of computer-aided diagnosis (CAD) system in this field. For example, compared to the other modalities, the quality of a US image is, currently, highly operator dependent; the conditions of examination should also directly affect the quality of US images. So far, these factors have hampered the application of deep learning-based technology in the field of US diagnosis. However, the development of CAD and US technologies will contribute to an increase in diagnostic quality, facilitate the development of remote medicine, and reduce the costs in the national health care through the early diagnosis of diseases. From this point of view, it may have a large enough potential to induce a paradigm shift in the field of US imaging and diagnosis of liver diseases.

Keywords

Ultrasonography Liver disease Computer-aided diagnosis Deep learning Artificial intelligence 

Notes

Acknowledgements

This work was supported by Japan Agency for Medical Research and Development under the Grant number 18lk1010030h0001 (M. Kudo, T. Shiina, N. Nishida), and partially supported by Grant-in-Aid for Scientific Research (KAKENHI: 16K09382) from the Japanese Society for the Promotion of Science (N. Nishida) and a grant from the Smoking Research Foundation (N. Nishida).

Compliance with ethical standards

Conflict of interest

The authors have no conflicts of interest to disclose.

Ethical approval

This is not a research paper involving human participants and/or animals; informed consent is not required.

Informed consent

Informed consent is not required.

References

  1. 1.
    Kudo M. Breakthrough imaging in hepatocellular carcinoma. Liver Cancer 2016;5:47–54CrossRefPubMedGoogle Scholar
  2. 2.
    Makino Y, Imai Y, Igura T, Kogita S, Sawai Y, Fukuda K et al. Feasibility of extracted-overlay fusion imaging for intraoperative treatment evaluation of radiofrequency ablation for hepatocellular carcinoma. Liver Cancer 2016;5:269–279CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Kudo M. Defect reperfusion rmaging with sonazoid(R): a breakthrough in hepatocellular carcinoma. Liver Cancer 2016;5:1–7CrossRefPubMedGoogle Scholar
  4. 4.
    Park HJ, Choi BI, Lee ES, Park SB, Lee JB. How to differentiate borderline hepatic nodules in hepatocarcinogenesis: emphasis on imaging diagnosis. Liver Cancer 2017;6:189–203CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Mohammed HA, Yang JD, Giama NH, Choi J, Ali HM, Mara KC et al. Factors influencing surveillance for hepatocellular carcinoma in patients with liver cirrhosis. Liver Cancer 2017;6:126–136CrossRefGoogle Scholar
  6. 6.
    Minhas F, Sabih D, Hussain M. Automated classification of liver disorders using ultrasound images. J Med Syst 2012;36:3163–3172CrossRefPubMedGoogle Scholar
  7. 7.
    Esses SJ, Lu X, Zhao T, Shanbhogue K, Dane B, Bruno M et al. Automated image quality evaluation of T2-weighted liver MRI utilizing deep learning architecture. J Magn Reson Imaging 2018;47:723–728CrossRefPubMedGoogle Scholar
  8. 8.
    Yasaka K, Akai H, Abe O, Kiryu S. Deep learning with convolutional neural network for differentiation of liver masses at dynamic contrast-enhanced CT: a preliminary study. Radiology 2018;286:887–896CrossRefPubMedGoogle Scholar
  9. 9.
    Huang Q, Zhang F, Li X. Machine learning in ultrasound computer-aided diagnostic systems: a survey. Biomed Res Int 2018;2018:5137904PubMedPubMedCentralGoogle Scholar
  10. 10.
    Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 2016;316:2402–2410CrossRefGoogle Scholar
  11. 11.
    Ehteshami B, Veta M, van Diest PJ, van Ginneken B, Karssemeijer N, Litjens G et al. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA 2017;318:2199–2210CrossRefGoogle Scholar
  12. 12.
    Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017;542:115–118CrossRefPubMedGoogle Scholar
  13. 13.
    Kermany DS, Goldbaum M, Cai W, Valentim CCS, Liang H, Baxter SL, et al. Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 2018;172(1122–1131):e1129Google Scholar
  14. 14.
    Huang W, Li N, Lin Z, Huang GB, Zong W, Zhou J et al. Liver tumor detection and segmentation using kernel-based extreme learning machine. Conf Proc IEEE Eng Med Biol Soc 2013;2013:3662–3665PubMedGoogle Scholar
  15. 15.
    Mittal D, Kumar V, Saxena SC, Khandelwal N, Kalra N. Neural network based focal liver lesion diagnosis using ultrasound images. Comput Med Imaging Graph 2011;35:315–323CrossRefPubMedGoogle Scholar
  16. 16.
    Nishida N, Kudo M. Alteration of epigenetic profile in human hepatocellular carcinoma and its clinical implications. Liver Cancer 2014;3:417–427CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Virmani J, Kumar V, Kalra N, Khandelwal N. SVM-based characterization of liver ultrasound images using wavelet packet texture descriptors. J Digit Imaging 2013;26:530–543CrossRefPubMedGoogle Scholar
  18. 18.
    Virmani J, Kumar V, Kalra N, Khandelwal N. Characterization of primary and secondary malignant liver lesions from B-mode ultrasound. J Digit Imaging 2013;26:1058–1070CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Hwang YN, Lee JH, Kim GY, Jiang YY, Kim SM. Classification of focal liver lesions on ultrasound images by extracting hybrid textural features and using an artificial neural network. Biomed Mater Eng 2015;26(Suppl 1):S1599–S1611PubMedGoogle Scholar
  20. 20.
    Streba CT, Ionescu M, Gheonea DI, Sandulescu L, Ciurea T, Saftoiu A et al. Contrast-enhanced ultrasonography parameters in neural network diagnosis of liver tumors. World J Gastroenterol 2012;18:4427–4434CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Gatos I, Tsantis S, Spiliopoulos S, Skouroliakou A, Theotokas I, Zoumpoulis P et al. A new automated quantification algorithm for the detection and evaluation of focal liver lesions with contrast-enhanced ultrasound. Med Phys 2015;42:3948–3959CrossRefPubMedGoogle Scholar
  22. 22.
    Kondo S, Takagi K, Nishida M, Iwai T, Kudo Y, Ogawa K et al. Computer-aided diagnosis of focal liver lesions using contrast-enhanced ultrasonography with perflubutane microbubbles. IEEE Trans Med Imaging 2017;36:1427–1437CrossRefPubMedGoogle Scholar
  23. 23.
    Guo LH, Wang D, Qian YY, Zheng X, Zhao CK, Li XL et al. A two-stage multi-view learning framework based computer-aided diagnosis of liver tumors with contrast enhanced ultrasound images. Clin Hemorheol Microcirc 2018;69:343–354CrossRefPubMedGoogle Scholar
  24. 24.
    Subramanya MB, Kumar V, Mukherjee S, Saini M. A CAD system for B-mode fatty liver ultrasound images using texture features. J Med Eng Technol 2015;39:123–30CrossRefPubMedGoogle Scholar
  25. 25.
    Mihailescu DM, Gui V, Toma CI, Popescu A, Sporea I. Computer aided diagnosis method for steatosis rating in ultrasound images using random forests. Med Ultrason 2013;15:184–190CrossRefPubMedGoogle Scholar
  26. 26.
    Kim KB, Kim CW. Quantification of hepatorenal index for computer-aided fatty liver classification with self-organizing map and fuzzy stretching from ultrasonography. Biomed Res Int 2015;2015:535894PubMedPubMedCentralGoogle Scholar
  27. 27.
    Acharya UR, Raghavendra U, Fujita H, Hagiwara Y, Koh JE, Hong TJ et al. Automated characterization of fatty liver disease and cirrhosis using curvelet transform and entropy features extracted from ultrasound images. Comput Biol Med 2016;79:250–258CrossRefPubMedGoogle Scholar
  28. 28.
    Procopet B, Cristea VM, Robic MA, Grigorescu M, Agachi PS, Metivier S et al. Serum tests, liver stiffness and artificial neural networks for diagnosing cirrhosis and portal hypertension. Dig Liver Dis 2015;47:411–416CrossRefPubMedGoogle Scholar
  29. 29.
    Gatos I, Tsantis S, Spiliopoulos S, Karnabatidis D, Theotokas I, Zoumpoulis P et al. A machine-learning algorithm toward color analysis for chronic liver disease classification, employing ultrasound shear wave elastography. Ultrasound Med Biol 2017;43:1797–1810CrossRefPubMedGoogle Scholar
  30. 30.
    Zhang L, Li QY, Duan YY, Yan GZ, Yang YL, Yang RJ. Artificial neural network aided non-invasive grading evaluation of hepatic fibrosis by duplex ultrasonography. BMC Med Inform Decis Mak 2012;12:55CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Biswas M, Kuppili V, Edla DR, Suri HS, Saba L, Marinhoe RT et al. Symtosis: a liver ultrasound tissue characterization and risk stratification in optimized deep learning paradigm. Comput Methods Programs Biomed 2018;155:165–177CrossRefPubMedGoogle Scholar
  32. 32.
    Wang K, Lu X, Zhou H, Gao Y, Zheng J, Tong M, et al. Deep learning Radiomics of shear wave elastography significantly improved diagnostic performance for assessing liver fibrosis in chronic hepatitis B: a prospective multicentre study. Gut 2018.  https://doi.org/10.1136/gutjnl-2018-316204.CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Banzato T, Bonsembiante F, Aresu L, Gelain ME, Burti S, Zotti A. Use of transfer learning to detect diffuse degenerative hepatic diseases from ultrasound images in dogs: a methodological study. Vet J 2018;233:35–40CrossRefPubMedGoogle Scholar
  34. 34.
    Zeng YZ, Zhao YQ, Liao M, Zou BJ, Wang XF, Wang W. Liver vessel segmentation based on extreme learning machine. Phys Med 2016;32:709–716CrossRefPubMedGoogle Scholar
  35. 35.
    Nishida N, Kitano M, Sakurai T, Kudo M. Molecular mechanism and prediction of sorafenib chemoresistance in human hepatocellular carcinoma. Dig Dis 2015;33:771–779CrossRefPubMedGoogle Scholar
  36. 36.
    Nishida N, Arizumi T, Hagiwara S, Ida H, Sakurai T, Kudo M. MicroRNAs for the prediction of early response to sorafenib treatment in human hepatocellular carcinoma. Liver Cancer 2017;6:113–125CrossRefPubMedGoogle Scholar
  37. 37.
    Nishida N, Kudo M. Immune checkpoint blockade for the treatment of human hepatocellular carcinoma. Hepatol Res 2018;48:622–634CrossRefPubMedGoogle Scholar
  38. 38.
    Tarek M, Hassan ME, El-Sayed S. Diagnosis of focal liver diseases based on deep learning technique for ultrasound images. Arab J Sci Eng 2017;42:3127–3140CrossRefGoogle Scholar
  39. 39.
    Meng DZL, Cao G, Cao W, Zhang G, Hu B. Liver fibrosis classification based on trasnfer learning adn FCNet for ultrasound image. IEEE Access 2017;5:5804–5810Google Scholar
  40. 40.
    Liu X, Song JL, Wang SH, Zhao JW, Chen YQ. Learning to diagnose cirrhosis with liver capsule guided ultrasound image classification. Sensors 2017;17:E149(Basel).CrossRefPubMedGoogle Scholar

Copyright information

© Asian Pacific Association for the Study of the Liver 2019

Authors and Affiliations

  1. 1.Department of Gastroenterology and Hepatology, Faculty of MedicineKindai UniversityOsaka-sayamaJapan
  2. 2.Department of Human Health Sciences, Graduate School of MedicineKyoto UniversityKyotoJapan

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