Journal Home > Volume 2 , Issue 1
iLIVER 2023, 2(1): 73-87 https://doi.org/10.1016/j.iliver.2023.02.002
Review | Open Access | Issue | Published: 04 March 2023

When liver disease diagnosis encounters deep learning: Analysis, challenges, and prospects

Show Author's Information Yingjie Tiana,c,d,e,  Minghao Liub,c,d,  Yu Sunf,  Saiji Fug( )
School of Economics and Management, University of Chinese Academy of Sciences, Beijing, 100190, China
School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, 100049, China
Research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences, Beijing, 100190, China
Key Laboratory of Big Data Mining and Knowledge Management, University of Chinese Academy of Sciences, Beijing, 100190, China
MOE Social Science Laboratory of Digital Economic Forecasts and Policy Simulation at UCAS, Beijing, 100190, China
Tsinghua University Press, Beijing, 102611, China
School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing, 100876, China
Keywords:
Deep learning, Application, Liver, Data modality, Liver topic
Cite this article:
Tian Y, Liu M, Sun Y, et al. When liver disease diagnosis encounters deep learning: Analysis, challenges, and prospects. iLIVER, 2023, 2(1): 73-87. https://doi.org/10.1016/j.iliver.2023.02.002
Download citation
EndNote(RIS)
BibTeX

315

Views

17

Downloads

Citations

4

Crossref

N/A

WoS

N/A

Scopus

N/A

CSCD

Abstract Full text About this article

The liver is the second-largest organ in the human body and is essential for digesting food and removing toxic substances. Viruses, obesity, alcohol use, and other factors can damage the liver and cause liver disease. The diagnosis of liver disease used to depend on the clinical experience of doctors, which made it subjective, difficult, and time-consuming. Deep learning has made breakthroughs in various fields; thus, there is a growing interest in using deep learning methods to solve problems in liver research to assist doctors in diagnosis and treatment. In this paper, we provide an overview of deep learning in liver research using 139 papers from the last 5 years. We also show the relationship between data modalities, liver topics, and applications in liver research using Sankey diagrams and summarize the deep learning methods used for each liver topic, in addition to the relations and trends between these methods. Finally, we discuss the challenges of and expectations for deep learning in liver research.


menu
Abstract
Full text
Outline
About this article

When liver disease diagnosis encounters deep learning: Analysis, challenges, and prospects

Show Author's information Yingjie Tiana,c,d,e, Minghao Liub,c,d, Yu Sunf, Saiji Fug( )
School of Economics and Management, University of Chinese Academy of Sciences, Beijing, 100190, China
School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, 100049, China
Research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences, Beijing, 100190, China
Key Laboratory of Big Data Mining and Knowledge Management, University of Chinese Academy of Sciences, Beijing, 100190, China
MOE Social Science Laboratory of Digital Economic Forecasts and Policy Simulation at UCAS, Beijing, 100190, China
Tsinghua University Press, Beijing, 102611, China
School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing, 100876, China

Abstract

The liver is the second-largest organ in the human body and is essential for digesting food and removing toxic substances. Viruses, obesity, alcohol use, and other factors can damage the liver and cause liver disease. The diagnosis of liver disease used to depend on the clinical experience of doctors, which made it subjective, difficult, and time-consuming. Deep learning has made breakthroughs in various fields; thus, there is a growing interest in using deep learning methods to solve problems in liver research to assist doctors in diagnosis and treatment. In this paper, we provide an overview of deep learning in liver research using 139 papers from the last 5 years. We also show the relationship between data modalities, liver topics, and applications in liver research using Sankey diagrams and summarize the deep learning methods used for each liver topic, in addition to the relations and trends between these methods. Finally, we discuss the challenges of and expectations for deep learning in liver research.

Keywords: Deep learning, Application, Liver, Data modality, Liver topic

References(191)

[1]

Abdel-Misih Sherif RZ, Bloomston Mark. Liver anatomy. Surgical Clinics 2010; 90(4):643–53.
DOI Google Scholar
[2]

Sivakrishnan S, Pharm M. Liver disease overview. World Journal of Pharmacy and Pharmaceutical Sciences 2019;8(1):1385–95.
Google Scholar
[3]

Azer Samy A. Deep learning with convolutional neural networks for identification of liver masses and hepatocellular carcinoma: a systematic review. World J Gastrointest Oncol 2019;11(12):1218.
DOI Google Scholar
[4]

Gul Sidra, Khan Muhammad Salman, Bibi Asima, et al. Deep learning techniques for liver and liver tumor segmentation: a review. Comput Biol Med 2022:105620.
DOI Google Scholar
[5]

Survarachakan Shanmugapriya, Ray Prasad Pravda Jith, Naseem Rabia, et al. Deep learning for image-based liver analysis—a comprehensive review focusing on malignant lesions. Artif Intell Med 2022:102331.
DOI Google Scholar
[6]
Affane Abir, Lebre Marie-Ange, Mittal Utkarsh, et al. Literature review of deep learning models for liver vessels reconstruction. In: 2020 tenth international conference on image processing theory, tools and applications (IPTA). IEEE; 2020. p. 1–6.
DOI
[7]

Zhou Li-Qiang, Wang Jia-Yu, Song-Yuan Yu, et al. Artificial intelligence in medical imaging of the liver. World J Gastroenterol 2019;25(6):672.
DOI Google Scholar
[8]

Krizhevsky Alex, Sutskever Ilya, Hinton Geoffrey E. Imagenet classification with deep convolutional neural networks. Commun ACM 2017;60(6):84–90.
DOI Google Scholar
[9]

Gu Jiuxiang, Wang Zhenhua, Kuen Jason, et al. Recent advances in convolutional neural networks. Pattern Recogn 2018;77:354–77.
DOI Google Scholar
[10]

Yamashita Rikiya, Nishio Mizuho, Gian Do Richard Kinh, et al. Convolutional neural networks: an overview and application in radiology. Insights into imaging 2018;9(4):611–29.
DOI Google Scholar
[11]
Simonyan Karen, Zisserman Andrew. Very deep convolutional networks for largescale image recognition. arXiv preprint arXiv:1409.1556 2014.
[12]
Lin Min, Chen Qiang, Yan Shuicheng. Network in network. arXiv preprint arXiv: 1312.4400 2013.
[13]
Long Jonathan, Shelhamer Evan, Trevor Darrell. Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2015. p. 3431–40.
DOI
[14]

Tian Yingjie, Fu Saiji. A descriptive framework for the field of deep learning applications in medical images. Knowl Base Syst 2020;210:106445.
DOI Google Scholar
[15]

Yu Yong, Si Xiaosheng, Hu Changhua, et al. A review of recurrent neural networks: lstm cells and network architectures. Neural Comput 2019;31(7): 1235–70.
DOI Google Scholar
[16]

Vaswani Ashish, Shazeer Noam, Parmar Niki, et al. Attention is all you need. Adv Neural Inf Process Syst 2017;30.
Google Scholar
[17]
Devlin Jacob, Chang Ming-Wei, Lee Kenton, et al. Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv: 1810.04805 2018.
[18]
Alexey Dosovitskiy, Beyer Lucas, Alexander Kolesnikov, et al. An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv: 2010.11929 2020.
[19]

Liu Ze, Lin Yutong, Cao Yue, et al. Swin transformer: hierarchical vision transformer using shifted windows. Proceedings of the IEEE/CVF International Conference on Computer Vision 2021:10012–22.
DOI Google Scholar
[20]

Wu Zonghan, Pan Shirui, Chen Fengwen, et al. A comprehensive survey on graph neural networks. IEEE Transact Neural Networks Learn Syst 2020;32(1):4–24.
DOI Google Scholar
[21]

Zhang Si, Tong Hanghang, Xu Jiejun, et al. Graph convolutional networks: a comprehensive review. Computational Social Networks 2019;6(1):1–23.
DOI Google Scholar
[22]
Ronneberger Olaf, Fischer Philipp, Brox Thomas. U-net: convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention. Springer; 2015. p. 234–41.
DOI
[23]

Creswell Antonia, White Tom, Vincent Dumoulin, et al. Generative adversarial networks: an overview. IEEE Signal Process Mag 2018;35(1):53–65.
DOI Google Scholar
[24]

Goodfellow Ian, Pouget-Abadie Jean, Mirza Mehdi, et al. Generative adversarial networks. Commun ACM 2020;63(11):139–44.
DOI Google Scholar
[25]

Zhuang Fuzhen, Qi Zhiyuan, Duan Keyu, et al. A comprehensive survey on transfer learning. Proc IEEE 2020;109(1):43–76.
DOI Google Scholar
[26]
Von Rueden Laura, Mayer Sebastian, Beckh Katharina, et al. Informed machine learning–a taxonomy and survey of integrating knowledge into learning systems. arXiv preprint arXiv:1903.12394 2019.
[27]

Yasaka Koichiro, Akai Hiroyuki, Abe Osamu, et al. Deep learning with convolutional neural network for differentiation of liver masses at dynamic contrast-enhanced CT: a preliminary study. Radiology 2018;286(3):887–96.
DOI Google Scholar
[28]

Frid-Adar Maayan, Diamant Idit, Klang Eyal, et al. Gan-based synthetic medical image augmentation for increased CNN performance in liver lesion classification. Neurocomputing 2018;321:321–31.
DOI Google Scholar
[29]

Vivanti Refael, Leo Joskowicz, Lev-Cohain Naama, et al. Patient-specific and global convolutional neural networks for robust automatic liver tumor delineation in follow-up ct studies. Med Biol Eng Comput 2018;56(9):1699–713.
DOI Google Scholar
[30]
Todoroki Yoshihiro, Han Xian-Hua, Iwamoto Yutaro, et al. Detection of liver tumor candidates from CT images using deep convolutional neural networks. In: International conference on innovation in medicine and healthcare. Springer; 2017. p. 140–5.
DOI
[31]

Bharti Puja, Mittal Deepti, Ananthasivan Rupa. Preliminary study of chronic liver classification on ultrasound images using an ensemble model. Ultrason Imag 2018; 40(6):357–79.
DOI Google Scholar
[32]

Li Siqi, Jiang Huiyan, Yao Yu-dong, et al. Structure convolutional extreme learning machine and case-based shape template for Hcc nucleus segmentation. Neurocomputing 2018;312(9–26).
DOI Google Scholar
[33]
Zhang Fan, Yang Junlin, Nezami Nariman, et al. Liver tissue classification using an auto-context-based deep neural network with a multi-phase training framework. In: International Workshop on patch-based Techniques in medical imaging. Springer; 2018. p. 59–66.
DOI
[34]

Trivizakis Eleftherios, Manikis Georgios C, Nikiforaki Katerina, et al. Extending 2-d convolutional neural networks to 3-d for advancing deep learning cancer classification with application to mri liver tumor differentiation. IEEE journal of biomedical and health informatics 2018;23(3):923–30.
DOI Google Scholar
[35]

Hamm Charlie A, Wang Clinton J, Savic Lynn J, et al. Deep learning for liver tumor diagnosis part i: development of a convolutional neural network classifier for multi-phasic mri. Eur Radiol 2019;29(7):3338–47.
DOI Google Scholar
[36]

Wang Clinton J, Hamm Charlie A, Savic Lynn J, et al. Deep learning for liver tumor diagnosis part ii: convolutional neural network interpretation using radiologic imaging features. Eur Radiol 2019;29(7):3348–57.
DOI Google Scholar
[37]

Kutlu Hüseyin, Avcı Engin. A novel method for classifying liver and brain tumors using convolutional neural networks, discrete wavelet transform and long shortterm memory networks. Sensors 2019;19(9):1992.
DOI Google Scholar
[38]

Wang Kang, Mamidipalli Adrija, Retson Tara, et al. Automated ct and mri liver segmentation and biometry using a generalized convolutional neural network. Radiology: Artif Intell 2019;1(2):180022.
DOI Google Scholar
[39]

Özyurt Fatih, Tuncer Türker, Avci Engin, et al. A novel liver image classification method using per-ceptual hash-based convolutional neural network. Arabian J Sci Eng 2019;44(4):3173–82.
DOI Google Scholar
[40]

Lin Wenyi, Hasenstab Kyle, Moura Cunha Guilherme, et al. Comparison of handcrafted features and convolu-tional neural networks for liver mr image adequacy assessment. Sci Rep 2020;10(1):1–11.
DOI Google Scholar
[41]

Han Aiguo, Byra Michal, Heba Elhamy, et al. Noninvasive diagnosis of nonalcoholic fatty liver disease and quantification of liver fat with radiofrequency ultrasound data using one-dimensional convolutional neural networks. Radiology 2020;295(2):342.
DOI Google Scholar
[42]

Lee Jeong Hyun, Joo Ijin, Kang Tae Wook, et al. Deep learning with ultrasonography: automated classification of liver fibrosis using a deep convolutional neural network. Eur Radiol 2020;30(2):1264–73.
DOI Google Scholar
[43]

Lu Meng, Tian Yaoyu, Bu Sihang. Liver tumor segmentation based on 3d convolutional neural network with dual scale. J Appl Clin Med Phys 2020;21(1): 144–57.
DOI Google Scholar
[44]

Aghamohammadi Amirhossein, Ranjbarzadeh Ramin, Naiemi Fatemeh, et al. Tpcnn: two-path convolutional neural network for tumor and liver segmentation in ct images using a novel encoding approach. Expert Syst Appl 2021;183:115406.
DOI Google Scholar
[45]

Zhou Jiarong, Wang Wenzhe, Lei Biwen, et al. Automatic detection and classification of focal liver lesions based on deep convolutional neural networks: a preliminary study. Front Oncol 2021;10:581210.
DOI Google Scholar
[46]

Che Hui, Brown Lloyd G, Foran David J, et al. Liver disease classification from ultrasound using multi-scale cnn. Int J Comput Assist Radiol Surg 2021;16(9): 1537–48.
DOI Google Scholar
[47]
Hasegawa Ryo, Iwamoto Yutaro, Han Xianhua, et al. Automatic detection and segmentation of liver tumors in multi-phase ct images by phase attention mask rcnn. In: 2021 IEEE international conference on consumer electronics (ICCE). IEEE; 2021. p. 1–5.
DOI
[48]

Chung Minyoung, Lee Jingyu, Park Sanguk, et al. Liver segmentation in abdominal ct images via auto-context neural network and self-supervised contour attention. Artif Intell Med 2021;113:102023.
DOI Google Scholar
[49]

Ahmad Mubashir, Furqan Qadri Syed, Qadri Salman, et al. A lightweight convolutional neural network model for liver segmentation in medical diagnosis. Comput Intell Neurosci 2022:2022.
DOI Google Scholar
[50]
Humady Khaled, Al-Saeed Yasmeen, Eladawi Nabila, et al. Efficient liver segmentation with 3d cnn using computed tomography scans. arXiv preprint arXiv:2208.13271 2022.
[51]

Cunha Guilherme M, Delgado Timoteo I, Middleton Michael S, et al. Automated cnn–based analysis versus manual analysis for mr elastography in nonal-coholic fatty liver disease: intermethod agreement and fibrosis stage discriminative performance. Am J Roentgenol 2022;219(2):224–32.
DOI Google Scholar
[52]

Xie Xiwang, Pan Xipeng, Shao Feng, et al. Mci-net: multi-scale context integrated network for liver ct image segmentation. Comput Electr Eng 2022;101:108085.
DOI Google Scholar
[53]

Ben-Cohen Avi, Klang Eyal, Kerpel Ariel, et al. Fully convolutional network and sparsity-based dictionary learning for liver lesion detection in ct examinations. Neurocomputing 2018;275:1585–94.
DOI Google Scholar
[54]
Vorontsov Eugene, Tang An, Pal Chris, et al. Liver lesion segmentation informed by joint liver segmentation. In: 2018 IEEE 15th international symposium on biomedical imaging (ISBI 2018). IEEE; 2018. p. 1332–5.
DOI
[55]
Pang Yuwei, Hu Dong, Sun Min. A modified scheme for liver tumor segmentation based on cascaded fcns. In: Proceedings of the international conference on artificial intelligence, information processing and cloud computing; 2019. p. 1–6.
DOI
[56]

Guo Xiaotao, Schwartz Lawrence H, Zhao Binsheng. Automatic liver segmentation by integrating fully convolutional networks into active contour models. Med Phys 2019;46(10):4455–69.
DOI Google Scholar
[57]

Vorontsov Eugene, Cerny Milena, Régnier Philippe, et al. Deep learning for automated segmentation of liver lesions at ct in patients with colorectal cancer liver metastases. Radiology. Artificial intelligence 2019;1(2).
DOI Google Scholar
[58]
Qi Zeng, Karimi Davood, Pang Emily HT, et al. Liver segmentation in magnetic resonance imaging via mean shape fitting with fully convolutional neural networks. In: International conference on medical image computing and computer-assisted in-tervention. Springer; 2019. p. 246–54.
DOI
[59]
Zhang Yue, Wu Jiong, Jiang Benxiang, et al. Deep learning and unsupervised fuzzy c-means based level-set segmentation for liver tumor. In: 2020 IEEE 17th international symposium on biomedical imaging (ISBI). IEEE; 2020. p. 1193–6.
DOI
[60]

Ibrahim Alirr Omar. Deep learning and level set approach for liver and tumor segmentation from ct scans. J Appl Clin Med Phys 2020;21(10):200–9.
DOI Google Scholar
[61]

Shukla Piyush Kumar, Zakariah Mohammed, Hatam-leh Wesam Atef, et al. Aidriven novel approach for liver cancer screening and prediction using cascaded fully convolutional neural network. Journal of Healthcare Engineering 2022: 2022.
DOI Google Scholar
[62]
Byappanahalli Suresha Pradyumna, Wang Yunlong, Xiao Cao, et al. A deep learning approach for classifying nonalcoholic steatohepatitis patients from nonalcoholic fatty liver disease patients using electronic medical records. In: Explainable AI in healthcare and medicine. Springer; 2021. p. 107–13.
DOI
[63]

Ioannou George N, Tang Weijing, Beste Lauren A, et al. Assessment of a deep learning model to predict hepatocellular carcinoma in patients with hepatitis c cirrhosis. JAMA Netw Open 2020;3(9):e2015626. e2015626.
DOI Google Scholar
[64]

Navaneethakrishnan Mariappan, Vairamuthu Subbiah, Parthasarathy Govindaswamy, et al. Atom search-jaya-based deep recurrent neural network for liver cancer detection. IET Image Process 2021;15(2):337–49.
DOI Google Scholar
[65]
Dong Liang, Lin Lanfen, Hu Hongjie, et al. Combining convolutional and recurrent neural networks for classification of focal liver lesions in multi-phase ct images. In: International conference on medical image computing and computer-assisted intervention. Springer; 2018. p. 666–75.
DOI
[66]
Dong Liang, Lin Lanfen, Chen Xiao, et al. Multi-stream scale-insensitive convolutional and recurrent neural networks for liver tumor detection in dynamic ct images. In: 2019 IEEE international conference on image processing (ICIP). IEEE; 2019. p. 794–8.
DOI
[67]

Phan Dinh-Van, Chan Chien-Lung, Adams Li Ai-Hsien, et al. Liver cancer prediction in a viral hepatitis cohort: a deep learning approach. Int J Cancer 2020; 147(10):2871–8.
DOI Google Scholar
[68]

Wang Guangyu, Li Zhibin, Li Guangjun, et al. Real-time liver tracking algorithm based on lstm and svr networks for use in surface-guided radiation therapy. Radiat Oncol 2021;16(1):1–12.
DOI Google Scholar
[69]

Zheng Rencheng, Wang Qidong, Lv Shuangzhi, et al. Automatic liver tumor segmentation on dynamic contrast enhanced mri using 4d informa-tion: deep learning model based on 3d convolution and convolutional lstm. IEEE Trans Med Imag 2022;41(10):2965–76.
DOI Google Scholar
[70]

Kashala Kabe Gedeon, Song Yuqing, Liu Zhe. Firenet-mlstm for classifying liver lesions by using deep features in ct images. Multimed Tool Appl 2022;81(2): 1607–23.
DOI Google Scholar
[71]
Wu Mian, Qian Yinling, Liao Xiangyun, et al. Hepatic vessel segmentation based on 3dswin-transformer with inductive biased multi-head self-attention. arXiv preprint arXiv:2111.03368 2021.
[72]
Demir Ugur, Zhang Zheyuan, Wang Bin, et al. Transformer based generative adversarial network for liver segmentation. arXiv preprint arXiv:2205.10663 2022.
[73]

Li Lingyun, Ma Hongbing. Rdctrans u-net: a hybrid variable architecture for liver CT image segmentation. Sensors 2022;22(7):2452.
DOI Google Scholar
[74]

Di Shuanhu, Zhao Yuqian, Liao Miao, et al. Td-net: a hybrid end-to-end network for automatic liver tumor segmentation from CT images. IEEE Journal of Biomedical and Health Informatics 2023;27(3):1163-1172.
DOI Google Scholar
[75]
Ali Hatamizadeh, Xu Ziyue, Dong Yang, et al. Unetformer: a unified vision transformer model and pre-training framework for 3d medical image segmentation. arXiv preprint arXiv:2204.00631 2022.
[76]
Chen Wei, Ren Shenghan, Guo Kaitai, et al. High-resolution swin transformer for automatic medical image segmentation. arXiv preprint arXiv:2207.11553 2022.
DOI
[77]

Wang Bo, Wang Fang. Pengwei Dong, et al. Multiscale transunet++: dense hybrid u-net with transformer for medical image segmentation. Signal. Image and Video Processing 2022:1–8.
DOI Google Scholar
[78]

Dadoun Hind, Rousseau Anne-Laure, de Kerviler Eric, et al. Deep learning for the detection, localization, and characterization of focal liver lesions on abdominal us images. Radiology: Artif Intell 2022;4(3):e210110.
DOI Google Scholar
[79]
Hu Yaqi, Yang Xiaoniu, Tian Xiaolin. A new approach for liver plus its tumor segmentation in Ct image by transnunet. In: 2022 IEEE international conference on artificial intelligence and computer applications (ICAICA). IEEE; 2022. p. 603–8.
DOI
[80]

Jiang Huiyan, Shi Tianyu, Bai Zhiqi, et al. AHCNet: an application of attention mechanism and hybrid connection for liver tumor segmentation in CT volumes. IEEE Access 2019;7:24898–909.
DOI Google Scholar
[81]

Schmauch B, Herent P, Jehanno P, et al. Diagnosis of focal liver lesions from ultrasound using deep learning. Diagnostic and Interventional Imaging 2019; 100(4):227–33.
DOI Google Scholar
[82]

Fan Tongle, Wang Guanglei, Li Yan, et al. Ma-net: a multi-scale attention network for liver and tumor segmentation. IEEE Access 2020;8:179656–65.
DOI Google Scholar
[83]
Chen Li, Tan Yusong, Chen Wei, et al. Attention unet++: a nested attention-aware u-net for liver CT image segmentation. In: 2020 IEEE international conference on image processing (ICIP). IEEE; 2020. p. 345–9.
[84]

Jin Qiangguo, Meng Zhaopeng, Sun Changming, et al. Ra-unet: a hybrid deep attention-aware network to extract liver and tumor in CT scans. Front Bioeng Biotechnol 2020:1471.
DOI Google Scholar
[85]

Yan Qingsen, Wang Bo, Zhang Wei, et al. Attention-guided deep neural network with multi-scale feature fusion for liver vessel segmentation. IEEE Journal of Biomedical and Health Informatics 2020;25(7):2629–42.
DOI Google Scholar
[86]

Lal Shyam, Das Devikalyan, Kumar Alabhya, et al. Nucleisegnet: robust deep learning architecture for the nuclei segmentation of liver cancer histopathology images. Comput Biol Med 2021;128:104075.
DOI Google Scholar
[87]

Wang Jinke, Zhang Xiangyang, Lv Peiqing, et al. Automatic liver segmentation using efficient net and attention-based residual u-net in ct. J Digit Imag 2022:1–15.
Google Scholar
[88]

Zhang Chi, Lu Jingben, Hua Qianqian, et al. SAA-net: U-shaped network with scale-axis-attention for liver tumor segmentation. Biomed Signal Process Control 2022;73:103460.
DOI Google Scholar
[89]

Bi Rongrong, Ji Chunlei, Yang Zhipeng, et al. Residual based attention-unet combing DAC and RMP modules for automatic liver tumor segmentation in CT. Math Biosci Eng 2022;19(5):4703–18.
DOI Google Scholar
[90]
Hille Georg, Agrawal Shubham, Wybranski Christian, et al. Joint liver and hepatic lesion segmentation using a hybrid CNN with transformer layers. arXiv preprint arXiv:2201.10981 2022.
[91]
Tong Fei, Nakao Megumi, Wu Shuqiong, et al. X-ray2shape: reconstruction of 3d liver shape from a single 2d projection image. In: 2020 42nd annual international conference of the IEEE engineering in medicine & biology society (EMBC). IEEE; 2020. p. 1608–11.
DOI
[92]
Mo Shaocong, Cai Ming, Lin Lanfen, et al. Multimodal priors guided segmentation of liver lesions in MRI using mutual information based graph co-attention networks. In: International Conference on medical image computing and computer-assisted intervention. Springer; 2020. p. 429–38.
DOI
[93]
Wojciechowska Marta, Malacrino Stefano, Martin Natalia Garcia, et al. Early detection of liver fibrosis using graph convolutional networks. In: International conference on medical image computing and computer-assisted intervention. Springer; 2021. p. 217–26.
DOI
[94]

Xu Chenchu, Zhang Dong, Chong Jaron, et al. Synthesis of gadolinium-enhanced liver tumors on nonenhanced liver MR images using pixel-level graph reinforcement learning. Med Image Anal 2021;69:101976.
DOI Google Scholar
[95]

Shao Hua-Chieh, Wang Jing, Bai Ti, et al. Real-time liver tumor localization via a single X-ray projection using deep graph neural network-assisted biomechanical modeling. Phys Med Biol 2022;67(11):115009.
DOI Google Scholar
[96]

Zhang Ge, Peng Zhen, Yan Chaokun, et al. A novel liver cancer diagnosis method based on patient similarity network and densegcn. Sci Rep 2022;12(1):1–10.
DOI Google Scholar
[97]
Kim Yushin, Kim Jaehyeon, Lee Sejong, et al. Automatic hepatocellular carcinoma diagnosis using graph convolutional network. 2022 BMC Biomedical Engineering 2021;3(1):1–13. International Conference on Electronics, Information, and Commu-nication (ICEIC), pages 1–4. IEEE, 2022.
DOI
[98]

Li Xiaomeng, Chen Hao, Qi Xiaojuan, et al. H-denseunet: hybrid densely connected u-net for liver and tumor segmentation from CT volumes. IEEE Trans Med Imag 2018;37(12):2663–74.
DOI Google Scholar
[99]

Huang Qing, Sun Jinfeng, Ding Hui, et al. Robust liver vessel extraction using 3d u-net with variant dice loss function. Comput Biol Med 2018;101:153–62.
DOI Google Scholar
[100]

Liu Zhe, Song Yu-Qing, Sheng Victor S, et al. Liver CT sequence segmentation based with improved u-net and graph cut. Expert Syst Appl 2019;126:54–63.
DOI Google Scholar
[101]

Seo Hyunseok, Huang Charles, Bassenne Maxime, et al. Modified u-net (mu-net) with incorporation of object-dependent high level features for improved liver and liver-tumor segmentation in CT images. IEEE Trans Med Imag 2019;39(5): 1316–25.
DOI Google Scholar
[102]
Yu Wei, Fang Bin, Liu Yongqing, et al. Liver vessels segmentation based on 3d residual u-net. In: 2019 IEEE international conference on image processing (ICIP). IEEE; 2019. p. 250–4.
DOI
[103]
Sakboonyara Boonnatee, Taeprasartsit Pinyo. U-net and mean-shift histogram for efficient liver segmentation from CT images. In: 2019 11th international conference on knowledge and smart technology (KST). IEEE; 2019. p. 51–6.
DOI
[104]
Su Ting-Yu, Fang Yu-Hua. Automatic liver and spleen segmentation with CT images using multi-channel u-net deep learning approach. In: International conference on biomedical and health informatics. Springer; 2019. p. 33–41.
DOI
[105]
Jiang Tian, Liu Li, Shi Zhongchao, et al. Automatic couinaud segmentation from CT volumes on liver using GLC-unet. In: International workshop on machine learning in medical imaging. Springer; 2019. p. 274–82.
DOI
[106]
Liu Yiwei, Qi Na, Zhu Qing, et al. Cr-u-net: cascaded u-net with residual mapping for liver segmentation in CT images. In: 2019 IEEE visual communications and image processing (VCIP). IEEE; 2019. p. 1–4.
DOI
[107]

Chen Yilong, Wang Kai, Liao Xiangyun, et al. Channel-unet: a spatial channel-wise convolutional neural network for liver and tumors segmentation. Front Genet 2019;10:1110.
DOI Google Scholar
[108]
Awad Albishri Ahmed, Hussain Shah Syed Jawad, Lee Yugyung, et al. Cu-net: cascaded u-net model for automated liver and lesion segmentation and summarization. In: 2019 IEEE international conference on Bioinformatics and biomedicine (BIBM). IEEE; 2019. p. 1416–23.
DOI
[109]

Tran Song-Toan, Cheng Ching-Hwa, Liu Don-Gey. A multiple layer u-net, u n-net, for liver and liver tumor segmentation in ct. IEEE Access 2020;9:3752–64.
DOI Google Scholar
[110]

Song LI, Geoffrey KF, He Kaijian. Bottleneck feature supervised u-net for pixelwise liver and tumor segmentation. Expert Syst Appl 2020;145:113131.
DOI Google Scholar
[111]

Chen Wen-Fan, Ou Hsin-You, Liu Keng-Hao, et al. In-series u-net network to 3d tumor image reconstruction for liver hepatocellular carcinoma recognition. Diagnostics 2020;11(1):11.
DOI Google Scholar
[112]
Huang Huimin, Lin Lanfen, Tong Ruofeng, et al. Unet 3+: a full-scale connected u-net for medical image segmentation. In: ICASSP 2020-2020 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE; 2020. p. 1055–9.
DOI
[113]
Yodit Abebe Ayalew, Kinde Anlay Fante, and Mohammed Aliy Mohammed. Modified u-net for liver cancer segmentation from computed tomography images with a new class balancing method.
[114]

Wang Jinke, Lv Peiqing, Wang Haiying, et al. Sar-u-net: squeeze-and-excitation block and atrous spatial pyramid pooling based residual u-net for automatic liver segmentation in computed tomography. Comput Methods Progr Biomed 2021; 208:106268.
DOI Google Scholar
[115]
Wang Jinke, Zhang Xiangyang, Lv Peiqing, et al. Ear-u-net: efficient net and attention-based residual u-net for automatic liver segmentation in CT. arXiv preprint arXiv:2110.01014 2021.
[116]

Affane Abir, Adrian Kucharski, Paul Chapuis, et al. Segmentation of liver anatomy by combining 3d u-net approaches. Appl Sci 2021;11(11):4895.
DOI Google Scholar
[117]

Fan Tongle, Wang Guanglei, Wang Xia, et al. Msn-net: a multi-scale context nested u-net for liver segmentation. Signal, Image and Video Processing 2021;15(6): 1089–97.
DOI Google Scholar
[118]

Chi Jianning, Han Xiaoying, Wu Chengdong, et al. X-net: multi-branch unet-like network for liver and tumor segmentation from 3d abdominal CT scans. Neurocomputing 2021;459:81–96.
DOI Google Scholar
[119]
Islam Muhammad, Nawaz Khan Kaleem, Khan Muhammad Salman. Evaluation of preprocessing techniques for u-net based automated liver segmentation. In: 2021 international conference on artificial intelligence (ICAI). IEEE; 2021. p. 187–92.
DOI
[120]

Gao Qinhan, Mohamed Almekkawy. Ultrasound liver tumor segmentation with nested u-net and dynamic feature extraction. J Acoust Soc Am 2021;149(4). A115–A115.
DOI Google Scholar
[121]
Louye Elghazy Hagar, Waleed Fakhr Mohamed. Multi-modal multi-stream u-net model for liver segmentation. In: 2021 IEEE world AI IoT congress (AIIoT). IEEE; 2021. 0028–0033.
DOI
[122]
Mourya GK, Bhatia D, Gogoi M, et al. CT guided diagnosis: cascaded u-net for 3D segmentation of liver and tumor. In: IOP conference series: materials science and engineering, 1128. IOP Publishing; 2021, 012049.
DOI
[123]
Li Qiang, Song Hong, Zhang Weiwei, et al. CC-denseunet: densely connected u-net with criss-cross attention for liver and tumor segmentation in CT volumes. In: 2021 IEEE international conference on bioinformatics and biomedicine (BIBM). IEEE; 2021. p. 966–71.
DOI
[124]
Han Lin, Chen Yuanhao, Li Jiaming, et al. Liver segmentation with 2.5 d perpendicular unets. Comput Electr Eng 2021;91:107118.
DOI
[125]

Sun Junding, Hui Zhenkun, Tang Chaosheng, et al. Liver segmentation based on complementary features u-net. Vis Comput 2022:1–12.
DOI Google Scholar
[126]

Gao Qinhan, Mohamed Almekkawy. Asu-net++: a nested u-net with adaptive feature extractions for liver tumor segmentation. Comput Biol Med 2021;136: 104688.
DOI Google Scholar
[127]

Kushnure Devidas T, Talbar Sanjay N. Ms-unet: a multi-scale u-net with feature recalibration approach for automatic liver and tumor segmentation in CT images. Comput Med Imag Graph 2021;89:101885.
DOI Google Scholar
[128]

Jing Li, Ou Xue, Shen Nanyan, et al. Study on strategy of CT image sequence segmentation for liver and tumor based on u-net and bi-convlstm. Expert Syst Appl 2021;180:115008.
DOI Google Scholar
[129]

Wu Jiawei, Zhou Shengqiang, Zuo Songlin, et al. U-net combined with multi-scale attention mechanism for liver segmentation in CT images. BMC Med Inf Decis Making 2021;21(1):1–12.
DOI Google Scholar
[130]
Hong Luminzi, Wang Risheng, Lei Tao, et al. Qau-net: quartet attention u-net for liver and liver-tumor segmentation. In: 2021 IEEE international conference on multimedia and expo (ICME). IEEE; 2021. p. 1–6.
DOI
[131]

Khan Rayyan Azam, Luo Yigang, Wu Fang-Xiang. RMS-unet: residual multi-scale u-net for liver and lesion segmentation. Artif Intell Med 2022;124:102231.
DOI Google Scholar
[132]

Kalsoom Anum, Maqsood Muazzam, Yasmin Sadaf, et al. A computer-aided diagnostic system for liver tumor detection using modified u-net architecture. J Supercomput 2022;78(7):9668–90.
DOI Google Scholar
[133]

Kushnure Devidas T, Talbar Sanjay N. HFRU-net: high-level feature fusion and recalibration u-net for automatic liver and tumor segmentation in CT images. Comput Methods Progr Biomed 2022;213:106501.
DOI Google Scholar
[134]

Manjunath RV, Kwadiki Karibasappa. Modified u-net on CT images for automatic segmentation of liver and its tumor. Biomedical Engineering Advances 2022: 100043.
DOI Google Scholar
[135]

Rela Munipraveena, Rao Suryakari Nagaraja, Patil Ramana Reddy. A diagnosis system by u-net and deep neural network enabled with optimal feature selection for liver tumor detection using CT images. Multimed Tool Appl 2022:1–43.
DOI Google Scholar
[136]

Wu Yun, Shen Huaiyan, Tan Yaya, et al. Automatic liver tumor segmentation used the cascade multi-scale attention architecture method based on 3D u-net. Int J Comput Assist Radiol Surg 2022:1–8.
DOI Google Scholar
[137]
Hu Cui, Haiwei Pan, Kejia Zhang, "SCU-Net++: A Nested U-Net Based on Sharpening Filter and Channel Attention Mechanism", Wireless Communications and Mobile Computing, vol. 2022, Article ID 2848365, 8 pages, 2022.
DOI
[138]
Dickson Joel, Lincely Arul, Nineta Alice. A dual channel multiscale convolution u-net method for liver tumor segmentation from abdomen CT images. In: 2022 international conference on sustainable computing and data communication systems (ICSCDS). IEEE; 2022. p. 1624–8.
DOI
[139]
Naaqvi Zunaira, Akbar Shahzad, Hassan Syed Ale, et al. Detection of liver cancer through computed tomography images using deep convolutional neural networks. In: 2022 2nd international conference on digital futures and transformative technologies (ICoDT2). IEEE; 2022. p. 1–6.
DOI
[140]
Qiang Li, Hong Song, Fengbo Yang, et al. Densely connected u-net with criss-cross attention for automatic liver tumor segmentation in CT images. IEEE ACM Trans Comput Biol Bioinf, pages 1-12, 2022.
DOI
[141]

Jeong Jin Gyo, Choi Sangtae, Kim Young Jae, et al. Deep 3D attention CLSTM u-net based automated liver segmentation and volumetry for the liver transplantation in abdominal CT volumes. Sci Rep 2022;12(1):1–9.
DOI Google Scholar
[142]
Xiao Xiaojiao, Zhao Juanjuan, Yan Qiang, et al. Radiomics-guided GAN for segmentation of liver tumor without contrast agents. In: International conference on medical image computing and computer-assisted intervention. Springer; 2019. p. 237–45.
DOI
[143]

Xia Kaijian, Yin Hongsheng, Qian Pengjiang, et al. Liver semantic segmentation algorithm based on improved deep adversarial networks in combination of weighted loss function on abdominal CT images. IEEE Access 2019;7:96349–58.
DOI Google Scholar
[144]

Sandfort Veit, Yan Ke, Pickhardt Perry J, et al. Data augmentation using generative adversarial networks (cyclegan) to improve generalizability in CT segmentation tasks. Sci Rep 2019;9(1):1–9.
DOI Google Scholar
[145]
Han Zheng, Lin Lanfen, Hu Hongjie, et al. Semi-supervised segmentation of liver using adversarial learning with deep atlas prior. In: International conference on medical image computing and computer-assisted intervention. Springer; 2019. p. 148–56.
DOI
[146]

Chen Lei, Song Hong, Wang Chi, et al. Liver tumor segmentation in CT volumes using an adversarial densely connected network. BMC Bioinf 2019;20(16):1–13.
DOI Google Scholar
[147]

Zhao Jianfeng, Li Dengwang, Kassam Zahra, et al. Tripartite-gan: synthesizing liver contrast-enhanced MRI to improve tumor detection. Med Image Anal 2020; 63:101667.
DOI Google Scholar
[148]

Fu Jie, Kamal Singhrao, Cao Minsong, et al. Generation of abdominal synthetic CTs from 0.35T MR images using generative adversarial networks for MR-only liver radiotherapy. Biomedical Physics & Engineering Express 2020;6(1): 015033.
DOI Google Scholar
[149]

Doman Keisuke, Konishi Takaaki, Mekada Yoshito. Lesion image synthesis using dcgans for metastatic liver cancer detection. Deep Learning in Medical Image Analysis 2020:95–106.
DOI Google Scholar
[150]

Liu Yang, Lu Meng, Zhong Jianping. Magan: mask attention generative adversarial network for liver tumor CT image synthesis. Journal of Healthcare Engineering 2021:2021.
DOI Google Scholar
[151]

Jin Qiangguo, Cui Hui, Sun Changming, et al. Free-form tumor synthesis in computed tomography images via richer generative adversarial network. Knowl Base Syst 2021;218:106753.
DOI Google Scholar
[152]
Yang Yulin, Iwamoto Yutaro, Chen Yen-Wei, et al. Synthesizing contrast-enhanced computed tomography images with an improved conditional generative adversarial network. In: 2022 44th annual international Conference of the IEEE Engineering in medicine & biology society (EMBC). IEEE; 2022. p. 2097–100.
DOI
[153]
He Li, Iwamoto Yutaro, Han Xianhua, et al. An accurate unsupervised liver lesion detection method using pseudo-lesions. In: International conference on medical image computing and computer-assisted intervention. Springer; 2022. p. 214–23.
DOI
[154]

Kuang Haopeng, Yang Zhongwei, Zhang Xukun, et al. Hepatic vein and arterial vessel segmentation in liver tumor patients. Comput Intell Neurosci 2022:2022.
DOI Google Scholar
[155]

Duan Y-Y, Qin J, Qiu W-Q, et al. Performance of a generative adversarial network using ultrasound images to stage liver fibrosis and predict cirrhosis based on a deep-learning radiomics nomogram. Clin Radiol 2022;77(10):e723–31.
DOI Google Scholar
[156]

Byra Michał, Styczynski Grzegorz, Szmigielski Cezary, et al. Transfer learning with deep convolutional neural network for liver steatosis assessment in ultrasound images. Int J Comput Assist Radiol Surg 2018;13(12):1895–903.
DOI Google Scholar
[157]

Marinelli Brett, Kang Martin, Martini Michael, et al. Combination of active transfer learning and natural language processing to improve liver volumetry using surrogate metrics with deep learning. Radiology. Artificial intelligence 2019;1(1).
DOI Google Scholar
[158]

Yamada Akira, Oyama Kazuki, Fujita Sachie, et al. Dynamic contrast-enhanced computed tomography diagnosis of primary liver cancers using transfer learning of pretrained convolutional neural networks: is registration of multiphasic images necessary? Int J Comput Assist Radiol Surg 2019;14(8):1295–301.
DOI Google Scholar
[159]

Xue Li-Yun, Jiang Zhuo-Yun, Fu Tian-Tian, et al. Transfer learning radiomics based on multimodal ultrasound imaging for staging liver fibrosis. Eur Radiol 2020; 30(5):2973–83.
DOI Google Scholar
[160]

Nowak Sebastian, Mesropyan Narine, Faron Anton, et al. Detection of liver cirrhosis in standard t2-weighted MRI using deep transfer learning. Eur Radiol 2021;31(11):8807–15.
DOI Google Scholar
[161]

Zhang Huili, Guo Lehang, Wang Dan, et al. Multi-source transfer learning via multi-kernel support vector machine plus for b-mode ultrasound-based computeraided diagnosis of liver cancers. IEEE Journal of Biomedical and Health Informatics 2021;25(10):3874–85.
DOI Google Scholar
[162]

Constantinescu Elena Codruta, Udristoiu Anca-Loredana, Udristoiu SC, et al. Transfer learning with pre-trained deep convolutional neural networks for the automatic assessment of liver steatosis in ultrasound images. Medical Ultrasonography 2021;23(2):135–9.
DOI Google Scholar
[163]

Byra Michal, Han Aiguo, Boehringer Andrew S, et al. Liver fat assessment in multiview sonography using transfer learning with convolutional neural networks. J Ultrasound Med 2022;41(1):175–84.
DOI Google Scholar
[164]

Das Bihter, Toraman Suat. Deep transfer learning for auto-mated liver cancer gene recognition using spectrogram images of digitized DNA sequences. Biomed Signal Process Control 2022;72:103317.
DOI Google Scholar
[165]

Mitchell Donald G, Bruix Jordi, Sherman Morris, et al. Li-rads (liver imaging reporting and data system): summary, discussion, and consensus of the lirads management working group and future directions. Hepatology 2015;61(3): 1056–65.
DOI Google Scholar
[166]

Soler L, Hostettler A, Agnus V, et al. 3D image reconstruction for comparison of algorithm database: a patient specific anatomical and medical image database. IRCAD, Strasbourg, France, Tech. Rep 2010;1(1).
Google Scholar
[167]

Bilic Patrick, Christ Patrick, Li Hongwei Bran, et al. The liver tumor segmentation benchmark (lits). Med Image Anal 2022:102680.
Google Scholar
[168]

Donato Henrique, França Manuela, Candelária Isabel, et al. Liver MRI: from basic protocol to advanced techniques. Eur J Radiol 2017;93:30–9.
DOI Google Scholar
[169]

Kavur A Emre, Gezer N Sinem, Barış Mustafa, et al. Chaos challenge-combined (CT-MR) healthy abdominal organ segmentation. Med Image Anal 2021;69: 101950.
DOI Google Scholar
[170]

Virmani Jitendra, Kumar Vinod, Kalra Naveen, et al. SVM-based characterization of liver ultrasound images using wavelet packet texture descriptors. J Digit Imag 2013;26(3):530–43.
DOI Google Scholar
[171]

Lin Liang, Yang Wei, Li Chenglong, et al. Inference with collaborative model for interactive tumor segmentation in medical image sequences. IEEE Trans Cybern 2015;46(12):2796–809.
DOI Google Scholar
[172]

Rubin Raphael, David S Strayer, Rubin Emanuel, et al. Rubin's pathology: clinicopathologic foundations of medicine. Lippincott Williams & Wilkins; 2008.
Google Scholar
[173]

Kumar Neeraj, Verma Ruchika, Sharma Sanuj, et al. A dataset and a technique for generalized nuclear segmentation for computational pathology. IEEE Trans Med Imag 2017;36(7):1550–60.
DOI Google Scholar
[174]

Kumar Neeraj, Verma Ruchika, Anand Deepak, et al. A multi-organ nucleus segmentation challenge. IEEE Trans Med Imag 2019;39(5):1380–91.
Google Scholar
[175]

Moon Andrew M, Green Pamela K, Berry Kristin, et al. Transformation of hepatitis c antiviral treatment in a national healthcare system following the introduction of direct antiviral agents. Aliment Pharmacol Therapeut 2017; 45(9):1201–12.
DOI Google Scholar
[176]

Mokdad Ali A, López Alan D, Shahraz Saied, et al. Liver cirrhosis mortality in 187 countries between 1980 and 2010: a systematic analysis. BMC Med 2014;12(1): 1–24.
DOI Google Scholar
[177]

Zheng Rongshou, Qu Chunfeng, Zhang Siwei, et al. Liver cancer incidence and mortality in China: temporal trends and projections to 2030. Chin J Cancer Res 2018;30(6):571.
DOI Google Scholar
[178]

Karlsen Tom H, Sheron Nick, Zelber-Sagi Shira, et al. The EASL–lancet liver commission: protecting the next generation of Europeans against liver disease complications and premature mortality. Lancet 2022;399(10319):61–116.
DOI Google Scholar
[179]

Akinyemiju Tomi, Abera Semaw, Ahmed Muktar, et al. The burden of primary liver cancer and underlying etiologies from 1990 to 2015 at the global, regional, and national level: results from the global burden of disease study 2015. JAMA Oncol 2017;3(12):1683–91.
DOI Google Scholar
[180]

Liu Chun-Yu, Chen Kuen-Feng, Chen Pei-Jer. Treatment of liver cancer. Cold Spring Harborperspectives in Medicine 2015;5(9):a021535.
DOI Google Scholar
[181]

Jemal Ahmedin, Bray Freddie, Melissa M Center, et al. Global cancer statistics. CA: A Cancer Journal for Clinicians 2011;61(2):69–90.
DOI Google Scholar
[182]

Gatti Marco, Maino Cesare, Tore Davide, et al. Benign focal liver lesions: the role of magnetic resonance imaging. World J Hepatol 2022;14(5):923.
DOI Google Scholar
[183]

Marrero Jorge A, Ahn Joseph, Rajender K Reddy. Practice Parameters Committee of the American College of Gastroenterology, et al. ACG clinical guideline: the diagnosis and management of focal liver lesions. Official journal of the American College of Gastroenterology| ACG 2014;109(9):1328–47.
DOI Google Scholar
[184]

Rinella Mary E. Nonalcoholic fatty liver disease: a systematic review. JAMA 2015; 313(22):2263–73.
DOI Google Scholar
[185]

Clark Jeanne M, Brancati Frederick L, E Diehl Anna Mae. Non-alcoholic fatty liver disease: the most common cause of abnormal liver enzymes in the US population. Gastroenterology 2001;5(120):A65.
DOI Google Scholar
[186]

Aydın M Merve, Akçalı Kamil Can. Liver fibrosis. Turk J Gastroenterol 2018;29(1): 14.
DOI Google Scholar
[187]
Lu Rui, Marziliano Pina, Hua Thng Choon. Liver tumor volume estimation by semi-automatic segmentation method. In: 2005 IEEE engineering in medicine and biology 27th annual conference. IEEE; 2006. p. 3296–9.
[188]
Chen Xueying, Zhang Rong, Yan Pingkun. Feature fusion encoder decoder network for automatic liver lesion segmentation. In: 2019 IEEE 16th international symposium on biomedical imaging (ISBI 2019). IEEE; 2019. p. 430–3.
DOI
[189]

Loomba Rohit, J Sanyal Arun. The global NAFLD epidemic. Nat Rev Gastroenterol Hepatol 2013;10(11):686–90.
DOI Google Scholar
[190]

Targher Giovanni, Day Christopher P, Bonora Enzo. Risk of cardiovascular disease in patients with nonalcoholic fatty liver disease. N Engl J Med 2010;363(14): 1341–50.
DOI Google Scholar
[191]

Lin Steven C, Heba Elhamy, Wolfson Tanya, et al. Noninvasive diagnosis of nonalcoholic fatty liver disease and quantification of liver fat using a new quantitative ultrasound technique. Clin Gastroenterol Hepatol 2015;13(7): 1337–45.
DOI Google Scholar
Publication history
Copyright
Acknowledgements
Rights and permissions

Publication history

Received: 26 December 2022
Revised: 20 February 2023
Accepted: 21 February 2023
Published: 04 March 2023
Issue date: March 2023

Copyright

© 2023 The Author(s). Published by Elsevier Ltd on behalf of Tsinghua University Press.

Acknowledgements

Acknowledgment

The authors sincerely thank all of our colleagues at the Research Center on Fictitious Economy and Data of the Chinese Academy of Sciences for their technical assistance.

Rights and permissions

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Return