Literature DB >> 34264509

The overview of the deep learning integrated into the medical imaging of liver: a review.

Kailai Xiang1,2, Baihui Jiang3, Dong Shang4,5.   

Abstract

Deep learning (DL) is a recently developed artificial intelligent method that can be integrated into numerous fields. For the imaging diagnosis of liver disease, several remarkable outcomes have been achieved with the application of DL currently. This advanced algorithm takes part in various sections of imaging processing such as liver segmentation, lesion delineation, disease classification, process optimization, etc. The DL optimized imaging diagnosis shows a broad prospect instead of the pathological biopsy for the advantages of convenience, safety, and inexpensiveness. In this paper, we reviewed the published representative DL-related hepatic imaging works, described the general situation of this new-rising technology in medical liver imaging and explored the future direction of DL development.
© 2021. Asian Pacific Association for the Study of the Liver.

Entities:  

Keywords:  Artificial intelligence; Computed tomography; Convolutional neural network; Deep learning; Image segmentation; Imaging diagnosis; Lesion classification; Liver disease; Magnetic resonance imaging; Ultrasonography

Year:  2021        PMID: 34264509     DOI: 10.1007/s12072-021-10229-z

Source DB:  PubMed          Journal:  Hepatol Int        ISSN: 1936-0533            Impact factor:   6.047


  59 in total

1.  MR-based synthetic CT generation using a deep convolutional neural network method.

Authors:  Xiao Han
Journal:  Med Phys       Date:  2017-03-21       Impact factor: 4.071

Review 2.  Current status and perspectives for computer-aided ultrasonic diagnosis of liver lesions using deep learning technology.

Authors:  Naoshi Nishida; Makoto Yamakawa; Tsuyoshi Shiina; Masatoshi Kudo
Journal:  Hepatol Int       Date:  2019-02-21       Impact factor: 6.047

Review 3.  Overview of deep learning in medical imaging.

Authors:  Kenji Suzuki
Journal:  Radiol Phys Technol       Date:  2017-07-08

Review 4.  Diagnosis and staging of hepatocellular carcinoma (HCC): current guidelines.

Authors:  Carmen Ayuso; Jordi Rimola; Ramón Vilana; Marta Burrel; Anna Darnell; Ángeles García-Criado; Luis Bianchi; Ernest Belmonte; Carla Caparroz; Marta Barrufet; Jordi Bruix; Concepción Brú
Journal:  Eur J Radiol       Date:  2018-01-31       Impact factor: 3.528

Review 5.  CAD and AI for breast cancer-recent development and challenges.

Authors:  Heang-Ping Chan; Ravi K Samala; Lubomir M Hadjiiski
Journal:  Br J Radiol       Date:  2019-12-16       Impact factor: 3.039

6.  Development and Validation of a Deep Learning System for Staging Liver Fibrosis by Using Contrast Agent-enhanced CT Images in the Liver.

Authors:  Kyu Jin Choi; Jong Keon Jang; Seung Soo Lee; Yu Sub Sung; Woo Hyun Shim; Ho Sung Kim; Jessica Yun; Jin-Young Choi; Yedaun Lee; Bo-Kyeong Kang; Jin Hee Kim; So Yeon Kim; Eun Sil Yu
Journal:  Radiology       Date:  2018-09-04       Impact factor: 11.105

Review 7.  Interpretive Error in Radiology.

Authors:  Stephen Waite; Jinel Scott; Brian Gale; Travis Fuchs; Srinivas Kolla; Deborah Reede
Journal:  AJR Am J Roentgenol       Date:  2016-12-27       Impact factor: 3.959

8.  Ultrasonography as first line imaging for the diagnosis of positional plagiocephaly: our experience and literature review.

Authors:  Fiammetta Sertorio; Mattia Pacetti; Simone Schiaffino; Francesca Secci; Armando Cama; Alessandro Consales; Gian Michele Magnano
Journal:  Minerva Pediatr       Date:  2019-03-21       Impact factor: 1.312

9.  The liver.

Authors:  Elijah Trefts; Maureen Gannon; David H Wasserman
Journal:  Curr Biol       Date:  2017-11-06       Impact factor: 10.834

Review 10.  Role of liver biopsy in hepatocellular carcinoma.

Authors:  Luca Di Tommaso; Marco Spadaccini; Matteo Donadon; Nicola Personeni; Abubaker Elamin; Alessio Aghemo; Ana Lleo
Journal:  World J Gastroenterol       Date:  2019-10-28       Impact factor: 5.742

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  2 in total

1.  IRIS-Intelligent Rapid Interactive Segmentation for Measuring Liver Cyst Volumes in Autosomal Dominant Polycystic Kidney Disease.

Authors:  Collin Li; Dominick Romano; Sophie J Wang; Hang Zhang; Martin R Prince; Yi Wang
Journal:  Tomography       Date:  2022-02-09

2.  Automatic Detection of Liver Cancer Using Hybrid Pre-Trained Models.

Authors:  Esam Othman; Muhammad Mahmoud; Habib Dhahri; Hatem Abdulkader; Awais Mahmood; Mina Ibrahim
Journal:  Sensors (Basel)       Date:  2022-07-20       Impact factor: 3.847

  2 in total

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