Literature DB >> 27097589

Computer-aided Characterization and Diagnosis of Diffuse Liver Diseases Based on Ultrasound Imaging: A Review.

Puja Bharti1, Deepti Mittal1, Rupa Ananthasivan2.   

Abstract

Diffuse liver diseases, such as hepatitis, fatty liver, and cirrhosis, are becoming a leading cause of fatality and disability all over the world. Early detection and diagnosis of these diseases is extremely important to save lives and improve effectiveness of treatment. Ultrasound imaging, a noninvasive diagnostic technique, is the most commonly used modality for examining liver abnormalities. However, the accuracy of ultrasound-based diagnosis depends highly on expertise of radiologists. Computer-aided diagnosis systems based on ultrasound imaging assist in fast diagnosis, provide a reliable "second opinion" for experts, and act as an effective tool to measure response of treatment on patients undergoing clinical trials. In this review, we first describe appearance of liver abnormalities in ultrasound images and state the practical issues encountered in characterization of diffuse liver diseases that can be addressed by software algorithms. We then discuss computer-aided diagnosis in general with features and classifiers relevant to diffuse liver diseases. In later sections of this paper, we review the published studies and describe the key findings of those studies. A concise tabular summary comparing image database, features extraction, feature selection, and classification algorithms presented in the published studies is also exhibited. Finally, we conclude with a summary of key findings and directions for further improvements in the areas of accuracy and objectiveness of computer-aided diagnosis.

Entities:  

Keywords:  artificial intelligence; classification; computer-aided characterization and diagnosis; diffuse liver diseases; feature extraction; medical image processing; ultrasound imaging

Year:  2016        PMID: 27097589     DOI: 10.1177/0161734616639875

Source DB:  PubMed          Journal:  Ultrason Imaging        ISSN: 0161-7346            Impact factor:   1.578


  3 in total

1.  Artificial Intelligence in Imaging: The Radiologist's Role.

Authors:  Daniel L Rubin
Journal:  J Am Coll Radiol       Date:  2019-09       Impact factor: 5.532

2.  Transfer learning with deep convolutional neural network for liver steatosis assessment in ultrasound images.

Authors:  Michał Byra; Grzegorz Styczynski; Cezary Szmigielski; Piotr Kalinowski; Łukasz Michałowski; Rafał Paluszkiewicz; Bogna Ziarkiewicz-Wróblewska; Krzysztof Zieniewicz; Piotr Sobieraj; Andrzej Nowicki
Journal:  Int J Comput Assist Radiol Surg       Date:  2018-08-09       Impact factor: 2.924

Review 3.  Noninvasive markers of liver steatosis and fibrosis after liver transplantation - Where do we stand?

Authors:  Ivana Mikolasevic; Sanja Stojsavljevic; Filip Blazic; Maja Mijic; Delfa Radic-Kristo; Toni Juric; Nadija Skenderevic; Mia Klapan; Andjela Lukic; Tajana Filipec Kanizaj
Journal:  World J Transplant       Date:  2021-03-18
  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.