nach oben

Hepatology International

Erschienen in:

21.02.2019 | Review Article

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

verfasst von: Naoshi Nishida, Makoto Yamakawa, Tsuyoshi Shiina, Masatoshi Kudo

Erschienen in: Hepatology International | Ausgabe 4/2019

Einloggen, um Zugang zu erhalten

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.

Kudo M. Breakthrough imaging in hepatocellular carcinoma. Liver Cancer 2016;5:47–54CrossRefPubMed

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;5:269–279CrossRefPubMedPubMedCentral

Kudo M. Defect reperfusion rmaging with sonazoid(R): a breakthrough in hepatocellular carcinoma. Liver Cancer 2016;5:1–7CrossRefPubMed

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;6:189–203CrossRefPubMedPubMedCentral

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;6:126–136CrossRef

Minhas F, Sabih D, Hussain M. Automated classification of liver disorders using ultrasound images. J Med Syst 2012;36:3163–3172CrossRefPubMed

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;47:723–728CrossRefPubMed

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;286:887–896CrossRefPubMed

Huang Q, Zhang F, Li X. Machine learning in ultrasound computer-aided diagnostic systems: a survey. Biomed Res Int 2018;2018:5137904PubMedPubMedCentral

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;316:2402–2410CrossRefPubMed

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;318:2199–2210CrossRef

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;542:115–118CrossRefPubMed

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):e1129

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;2013:3662–3665PubMed

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;35:315–323CrossRefPubMed

16.

Nishida N, Kudo M. Alteration of epigenetic profile in human hepatocellular carcinoma and its clinical implications. Liver Cancer 2014;3:417–427CrossRefPubMedPubMedCentral

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;26:530–543CrossRefPubMed

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;26:1058–1070CrossRefPubMedPubMedCentral

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–S1611PubMed

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;18:4427–4434CrossRefPubMedPubMedCentral

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;42:3948–3959CrossRefPubMed

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;36:1427–1437CrossRefPubMed

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;69:343–354CrossRefPubMed

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;39:123–30CrossRefPubMed

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;15:184–190CrossRefPubMed

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;2015:535894PubMedPubMedCentral

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;79:250–258CrossRefPubMed

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;47:411–416CrossRefPubMed

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;43:1797–1810CrossRefPubMed

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;12:55CrossRefPubMedPubMedCentral

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;155:165–177CrossRefPubMed

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.CrossRefPubMedPubMedCentral

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;233:35–40CrossRefPubMed

34.

Zeng YZ, Zhao YQ, Liao M, Zou BJ, Wang XF, Wang W. Liver vessel segmentation based on extreme learning machine. Phys Med 2016;32:709–716CrossRefPubMed

35.

Nishida N, Kitano M, Sakurai T, Kudo M. Molecular mechanism and prediction of sorafenib chemoresistance in human hepatocellular carcinoma. Dig Dis 2015;33:771–779CrossRefPubMed

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;6:113–125CrossRefPubMed

37.

Nishida N, Kudo M. Immune checkpoint blockade for the treatment of human hepatocellular carcinoma. Hepatol Res 2018;48:622–634CrossRefPubMed

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;42:3127–3140CrossRef

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;5:5804–5810

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).CrossRefPubMed

Titel: Current status and perspectives for computer-aided ultrasonic diagnosis of liver lesions using deep learning technology
verfasst von: Naoshi Nishida
Makoto Yamakawa
Tsuyoshi Shiina
Masatoshi Kudo
Publikationsdatum: 21.02.2019
Verlag: Springer India
Erschienen in: Hepatology International / Ausgabe 4/2019
Print ISSN: 1936-0533
Elektronische ISSN: 1936-0541
DOI: https://doi.org/10.1007/s12072-019-09937-4

Leitlinien kompakt für die Innere Medizin

Mit medbee Pocketcards sicher entscheiden.

^{Seit 2022 gehört die medbee GmbH zum Springer Medizin Verlag}

Kostenlos registrieren

Neu im Fachgebiet Innere Medizin

24.04.2024 | DGIM 2024 | Kongressbericht | Nachrichten

Update Innere Medizin

Bestellen Sie unseren Fach-Newsletter und bleiben Sie gut informiert.

Newsletter bestellen

Springer Medizin

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

Abstract

Leitlinien kompakt für die Innere Medizin

Neu im Fachgebiet Innere Medizin

Metformin rückt in den Hintergrund

Myokarditis nach Infekt – richtig schwierig wird es bei Profisportlern

Thrombophiliescreening – Wenn, dann richtig und nur bei therapeutischer Konsequenz

Bei Senioren mit Prostatakarzinom auf Anämie achten!

Update Innere Medizin

Springer Medizin

Abstract

Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten

Weitere Artikel der Ausgabe 4/2019

Biliary features in liver histology of children with autoimmune liver disease

New treatment-induced adverse effects we need to learn as modern hepatologists

Prognostic subclass of intrahepatic cholangiocarcinoma by integrative molecular–clinical analysis and potential targeted approach

Acute-on-chronic liver failure: consensus recommendations of the Asian Pacific association for the study of the liver (APASL): an update

The first Chinese guidelines on the Management of Ascites and its Related Complications in Cirrhosis: a great goal for a great country

Hepatology through the crystal ball

Leitlinien kompakt für die Innere Medizin

Neu im Fachgebiet Innere Medizin

Metformin rückt in den Hintergrund

Myokarditis nach Infekt – richtig schwierig wird es bei Profisportlern

Thrombophiliescreening – Wenn, dann richtig und nur bei therapeutischer Konsequenz

Bei Senioren mit Prostatakarzinom auf Anämie achten!

Update Innere Medizin