Computer-aided diagnosis of liver lesions using CT images: A systematic review

Nayantara, Vaidehi P and Kamath, Surekha and Manjunath, K N and Rajagopal, KV (2020) Computer-aided diagnosis of liver lesions using CT images: A systematic review. Computers in Biology and Medicine, 127. ISSN 0010-4825

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Background: Medical image processing has a strong footprint in radio diagnosis for the detection of diseases from the images. Several computer-aided systems were researched in the recent past to assist the radiologist in diagnosing liver diseases and reducing the interpretation time. The aim of this paper is to provide an overview of the state-of-the-art techniques in computer-assisted diagnosis systems to predict benign and malignant lesions using computed tomography images. Methods: The research articles published between 1998 and 2020 obtained from various standard databases were considered for preparing the review. The research papers include both conventional as well as deep learningbased systems for liver lesion diagnosis. The paper initially discusses the various hepatic lesions that are identifiable on computed tomography images, then the computer-aided diagnosis systems and their workflow. The conventional and deep learning-based systems are presented in stages wherein the various methods used for preprocessing, liver and lesion segmentation, radiological feature extraction and classification are discussed. Conclusion: The review suggests the scope for future, work as efficient and effective segmentation methods that work well with diverse images have not been developed. Furthermore, unsupervised and semi-supervised deep learning models were not investigated for liver disease diagnosis in the reviewed papers. Other areas to be explored include image fusion and inclusion of essential clinical features along with the radiological features for better classification accuracy.

Item Type: Article
Uncontrolled Keywords: Computer-aided detection/diagnosis Liver diseases Hemangioma Hepatocellular carcinoma Liver/lesion segmentation Feature extraction Classification Deep learning
Subjects: Engineering > MIT Manipal > Computer Science and Engineering
Engineering > MIT Manipal > Instrumentation and Control
Depositing User: MIT Library
Date Deposited: 18 Feb 2021 09:15
Last Modified: 18 Feb 2021 09:15

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