Deep learning approach for discrimination of liver lesions using nine time-phase images of contrast-enhanced ultrasound-Reference-Cited by-同舟云学术

Deep learning approach for discrimination of liver lesions using nine time-phase images of contrast-enhanced ultrasound

Published:2023-12-05 Issue:1 Volume:51 Page:83-93
ISSN:1346-4523
Container-title:Journal of Medical Ultrasonics
language:en
Short-container-title:J Med Ultrasonics

Author:

Kamiyama Naohisa^ORCID,Sugimoto Katsutoshi,Nakahara Ryuichi,Kakegawa Tatsuya,Itoi Takao

Publisher

Springer Science and Business Media LLC

Subject

Radiology, Nuclear Medicine and imaging,General Medicine

Link

https://link.springer.com/content/pdf/10.1007/s10396-023-01390-z.pdf

Reference26 articles.

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2. Kudo M, Kawamura Y, Hasegawa K, et al. Management of hepatocellular carcinoma in Japan: JSH consensus statements and recommendations 2021 update. Liver Cancer. 2021;10:181–223.

3. European Association for the Study of the Liver. Electronic address: easloffice@easloffice.eu, European Association for the Study of the Liver. EASL Clinical Practice Guidelines. EASL Clinical Practice Guidelines: management of hepatocellular carcinoma. J Hepatol. 2018; 69:182–236.

4. Kono Y, Lyshchik A, Cosgrove D, et al. Contrast enhanced ultrasound (CEUS) liver imaging reporting and data system (LI-RADS®): The official version by the American college of radiology (ACR). Ultraschall Med. 2017;38:85–6.

5. Yanagisawa K, Moriyasu F, Miyahara T, et al. Phagocytosis of ultrasound contrast agent microbubbles by kupffer cells. Ultrasound Med Biol. 2007;33:318–25.

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