Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

Keynote webinar | Spotlight on medication adherence

LIVE: Thursday 27th June 2024, 18:00-19:30 (CEST)
Join our expert panel to discover why you need to understand the drivers of non-adherence in your patients, and how you can optimize medication adherence in your clinics to drastically improve patient outcomes.
ADVANCED SEARCH
Log in
Top
European Radiology
Published in: European Radiology 10/2023

Open Access 12-05-2023 | Computed Tomography | Imaging Informatics and Artificial Intelligence

Performance and clinical applicability of machine learning in liver computed tomography imaging: a systematic review

Authors: Keyur Radiya, Henrik Lykke Joakimsen, Karl Øyvind Mikalsen, Eirik Kjus Aahlin, Rolv-Ole Lindsetmo, Kim Erlend Mortensen

Published in: European Radiology | Issue 10/2023

Login to get access

Abstract

Objectives

Machine learning (ML) for medical imaging is emerging for several organs and image modalities. Our objectives were to provide clinicians with an overview of this field by answering the following questions: (1) How is ML applied in liver computed tomography (CT) imaging? (2) How well do ML systems perform in liver CT imaging? (3) What are the clinical applications of ML in liver CT imaging?

Methods

A systematic review was carried out according to the guidelines from the PRISMA-P statement. The search string focused on studies containing content relating to artificial intelligence, liver, and computed tomography.

Results

One hundred ninety-one studies were included in the study. ML was applied to CT liver imaging by image analysis without clinicians’ intervention in majority of studies while in newer studies the fusion of ML method with clinical intervention have been identified. Several were documented to perform very accurately on reliable but small data. Most models identified were deep learning-based, mainly using convolutional neural networks. Potentially many clinical applications of ML to CT liver imaging have been identified through our review including liver and its lesion segmentation and classification, segmentation of vascular structure inside the liver, fibrosis and cirrhosis staging, metastasis prediction, and evaluation of chemotherapy.

Conclusion

Several studies attempted to provide transparent result of the model. To make the model convenient for a clinical application, prospective clinical validation studies are in urgent call. Computer scientists and engineers should seek to cooperate with health professionals to ensure this.

Key Points

• ML shows great potential for CT liver image tasks such as pixel-wise segmentation and classification of liver and liver lesions, fibrosis staging, metastasis prediction, and retrieval of relevant liver lesions from similar cases of other patients.
• Despite presenting the result is not standardized, many studies have attempted to provide transparent results to interpret the machine learning method performance in the literature.
• Prospective studies are in urgent call for clinical validation of ML method, preferably carried out by cooperation between clinicians and computer scientists.
Appendix
Available only for authorised users
Literature
1.
Baştanlar Y, Özuysal M (2014) Introduction to machine learning. In: Yousef M, Allmer J (eds) miRNomics: MicroRNA Biology and Computational Analysis. Humana Press, Totowa, NJ, pp 105–128
2.
Muehlematter UJ, Daniore P, Vokinger KN (2021) Approval of artificial intelligence and machine learning-based medical devices in the USA and Europe (2015–20): a comparative analysis. Lancet Digit Health 3:e195–e203PubMed
3.
4.
Rubin GD (2014) Computed tomography: revolutionizing the practice of medicine for 40 years. Radiology 273:S45–S74PubMed
5.
Cardobi N, Dal Palu A, Pedrini F et al (2021) An overview of artificial intelligence applications in liver and pancreatic imaging. Cancers 13:11
6.
Fu X, Cai N, Huang K et al (2019) M-Net: a novel U-Net with multi-stream feature fusion and multi-scale dilated convolutions for bile ducts and hepatolith segmentation. IEEE Access 7:148645–148657
7.
Decharatanachart P, Chaiteerakij R, Tiyarattanachai T, Treeprasertsuk S (2021) Application of artificial intelligence in chronic liver diseases: a systematic review and meta-analysis. BMC Gastroenterol 21:10PubMedPubMedCentral
8.
Lee S, Choe EK, Kim SY, Kim HS, Park KJ, Kim D (2020) Liver imaging features by convolutional neural network to predict the metachronous liver metastasis in stage I-III colorectal cancer patients based on preoperative abdominal CT scan. BMC Bioinf 21:382
9.
Taghavi M, Trebeschi S, Simões R et al (2021) Machine learning-based analysis of CT radiomics model for prediction of colorectal metachronous liver metastases. Abdom Radiol (NY) 46:249–256PubMed
10.
Aggarwal R, Sounderajah V, Martin G et al (2021) Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis. NPJ Digital Med 4:65
11.
Zhou LQ, Wang JY, Yu SY et al (2019) Artificial intelligence in medical imaging of the liver. World J Gastroenterol 25:672–682PubMedPubMedCentral
12.
13.
Azer SA (2019) Deep learning with convolutional neural networks for identification of liver masses and hepatocellular carcinoma: a systematic review. World J Gastrointest Oncol 11:1218–1230PubMedPubMedCentral
14.
Schiavo JH (2019) PROSPERO: an international register of systematic review protocols. Med Ref Serv Q 38:171–180PubMed
15.
McInnes MDF, Moher D, Thombs BD et al (2018) Preferred Reporting Items for a Systematic Review and Meta-analysis of diagnostic test accuracy studies: the PRISMA-DTA statement. JAMA 319:388–396PubMed
16.
Cumpston M, Li T, Page MJ et al (2019) Updated guidance for trusted systematic reviews: a new edition of the Cochrane Handbook for Systematic Reviews of Interventions. Cochrane Database Syst Rev 10:Ed000142PubMed
17.
de Hond AAH, Leeuwenberg AM, Hooft L et al (2022) Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review. NPJ Digit Med 5:2PubMedPubMedCentral
18.
Haddaway NR, Page MJ, Pritchard CC, McGuinness LA (2022) PRISMA2020: an R package and Shiny app for producing PRISMA 2020-compliant flow diagrams, with interactivity for optimised digital transparency and Open Synthesis. Campbell Syst Rev 18:e1230PubMedPubMedCentral
19.
Mubashir A, Yuan D, Syed Furqan Q, Jian Y (2019) Convolutional-neural-network-based feature extraction for liver segmentation from CT imagesProcSPIE, pp 1117934
20.
Ahn Y, Yoon JS, Lee SS et al (2020) Deep learning algorithm for automated segmentation and volume measurement of the liver and spleen using portal venous phase computed tomography images. Korean J Radiol 21:987–997PubMedPubMedCentral
21.
Bhavya A, Aditya B, Karthik K (2018) Automatic and fast CT liver segmentation using sparse ensemble with machine learned contextsProcSPIE, pp 105740L
22.
Albishri AA, Shah SJH, Lee Y (2019) CU-Net: cascaded U-Net model for automated liver and lesion segmentation and summarization. In: IEEE International Conference on Bioinformatics and Biomedicine (BIBM), San Diego, pp 1416–1423
23.
Ali L, Khelil K, Wajid SK et al (2017) Machine learning based computer-aided diagnosis of liver tumour. In: IEEE 16th International Conference on Cognitive Informatics and Cognitive Computing (ICCI*CC), Oxford, pp 139–114
24.
Alirr OI (2020) Deep learning and level set approach for liver and tumor segmentation from CT scans. J Appl Clin Med Phys 21:200–209PubMedPubMedCentral
25.
Astono I, Welsh JS, Chalup S (2018) Adjacent network for semantic segmentation of liver CT scans. In: 18th IEEE International Conference on Bioinformatics and Bioengineering, Taichung, pp 35–40
26.
Ben-Cohen A, Diamant I, Klang E, Amitai M, Greenspan H (2016) Fully convolutional network for liver segmentation and lesions detection. In: 2nd International Workshop on Deep Learning in Medical Image Analysis (DLMIA) / 1st International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis (LABELS). Springer International Publishing Ag, Athens, pp 77–85
27.
Bevilacqua V, Brunetti A, Trotta GF et al (2017) A novel approach for hepatocellular carcinoma detection and classification based on triphasic CT Protocol2017 IEEE Congress on Evolutionary Computation (CEC), pp 1856–1863
28.
Bhole C, Morsillo N, Pal C (2011) 3D segmentation in CT imagery with conditional random fields and histograms of oriented gradients. In: Suzuki K, Wang F, Shen D, Yan P (eds) Machine Learning in Medical Imaging. Springer, Berlin Heidelberg, Berlin, Heidelberg, pp 326–334
29.
Budak U, Guo Y, Tanyildizi E, Sengur A (2020) Cascaded deep convolutional encoder-decoder neural networks for efficient liver tumor segmentation. Med Hypotheses 134:8
30.
Cai J (2019) Segmentation and diagnosis of liver carcinoma based on adaptive scale-kernel fuzzy clustering model for CT images. J Med Syst 43:322PubMed
31.
Chen Y, Wang K, Liao X et al (2019) Channel-Unet: a spatial channel-wise convolutional neural network for liver and tumors segmentation. Front Gen 10
32.
Chlebus G, Schenk A, Moltz JH, van Ginneken B, Hahn HK, Meine H (2018) Automatic liver tumor segmentation in CT with fully convolutional neural networks and object-based postprocessing. Sci Rep 8:15497PubMedPubMedCentral
33.
Choi KJ, Jang JK, Lee SS et al (2018) Development and validation of a deep learning system for staging liver fibrosis by using contrast agent-enhanced CT images in the liver. Radiol 289:688–697
34.
Chung M, Lee J, Lee M, Lee J, Shin Y-G (2020) Deeply self-supervised contour embedded neural network applied to liver segmentation. Comput Methods Programs Biomed 192:105447PubMed
35.
Danciu M, Gordan M, Florea C, Orghidan R, Sorantin E, Vlaicu A (2013) A hybrid 3D learning-and-interaction-based segmentation approach applied on CT liver volumes. Radioeng 22:100–113
36.
Danciu M, Gordan M, Florea C, Vlaicu A (2012) 3D DCT supervised segmentation applied on liver volumes 2012. 35th International Conference on Telecommunications and Signal Processing (TSP), pp 779–783
37.
Delmoral JC, Costa DC, Borges D, Tavares JMRS (2019) Segmentation of pathological liver tissue with dilated fully convolutional networks: a preliminary study2019 IEEE 6th Portuguese Meeting on Bioengineering (ENBENG), pp 1–4
38.
Dong X, Zhou Y, Wang L, Peng J, Lou Y, Fan Y (2020) Liver cancer detection using hybridized fully convolutional neural network based on deep learning framework. IEEE Access 8:129889–129898
39.
Dou Q, Yu LQ, Chen H et al (2017) 3D deeply supervised network for automated segmentation of volumetric medical images. Med Image Anal 41:40–54
40.
Guo X, Schwartz LH, Zhao B (2019) Automatic liver segmentation by integrating fully convolutional networks into active contour models. Med Phys 46:4455–4469PubMed
41.
He B, Huang C, Sharp G et al (2016) Fast automatic 3D liver segmentation based on a three-level AdaBoost-guided active shape model. Med Phys 43:2421PubMed
42.
Heker M, Ben-Cohen A, Greenspan H (2019) Hierarchical fine-tuning for joint liver lesion segmentation and lesion classification in CT2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp 895–898
43.
Hu P, Wu F, Peng J, Liang P, Kong D (2016) Automatic 3D liver segmentation based on deep learning and globally optimized surface evolution. Phys Med Biol 61:8676–8698PubMed
44.
Huang W, Tan ZM, Lin Z et al (2012) A semi-automatic approach to the segmentation of liver parenchyma from 3D CT images with extreme learning machine. In: 34th Annual International Conference of the IEEE Engineering-in-Medicine-and-Biology-Society (EMBS). IEEE, San Diego, pp 3752–3755
45.
Ji H, He J, Yang X, Deklerck R, Cornelis J (2013) ACM-based automatic liver segmentation from 3-D CT images by combining multiple atlases and improved mean-shift techniques. IEEE J Biomed Health Inform 17:690–698PubMed
46.
Jiang H, Li S, Li S (2018) Registration-based organ positioning and joint segmentation method for liver and tumor segmentation. Biomed Res Int 2018:8536854PubMedPubMedCentral
47.
Jiang H, Shi T, Bai Z, Huang L (2019) AHCNet: an application of attention mechanism and hybrid connection for liver tumor segmentation in CT volumes. IEEE Access 7:24898–24909
48.
Jin X, Ye H, Li L, Xia Q (2017) Image segmentation of liver CT based on fully convolutional network2017 10th International Symposium on Computational Intelligence and Design (ISCID), pp 210–213
49.
Kavur AE, Gezer NS, Barış M et al (2020) Comparison of semi-automatic and deep learning-based automatic methods for liver segmentation in living liver transplant donors. Diagn Interv Radiol 26:11–21PubMed
50.
Kumar A, Dyer S, Kim J et al (2016) Adapting content-based image retrieval techniques for the semantic annotation of medical images. Comput Med Imaging Graph 49:37–45PubMed
51.
Zheng H, Lin L, Hu H et al (2019) Semi-supervised segmentation of liver using adversarial learning with deep atlas prior. In: Shen D, Liu T, Peters TM et al (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. Springer International Publishing, Cham, pp 148–156
52.
Zhang R, Zhou Z, Wu W, Lin CC, Tsui PH, Wu S (2018) An improved fuzzy connectedness method for automatic three-dimensional liver vessel segmentation in CT images. J Healthc Eng 2018:2376317PubMedPubMedCentral
53.
Zhang L, Xu L (2018) An automatic liver segmentation algorithm for CT images U-net with separated paths of feature extraction2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC), pp 294–298
54.
Xu W, Liu H, Wang X, Qian Y (2019) Liver segmentation in CT based on ResUNet with 3D probabilistic and geometric post process2019 IEEE 4th International Conference on Signal and Image Processing (ICSIP), pp 685–689
55.
Xi XF, Wang L, Sheng VS, Cui Z, Fu B, Hu F (2020) Cascade U-ResNets for simultaneous liver and lesion segmentation. IEEE Access 8:68944–68952
56.
Xin S, Shi H, Jide A, Zhu M, Ma C, Liao H (2020) Automatic lesion segmentation and classification of hepatic echinococcosis using a multiscale-feature convolutional neural network. Med Biol Eng Comput 58:659–668PubMed
57.
Xia K, Yin H, Qian P, Jiang Y, Wang S (2019) Liver semantic segmentation algorithm based on improved deep adversarial networks in combination of weighted loss function on abdominal CT images. IEEE Access 7:96349–96358
58.
Winkel DJ, Weikert TJ, Breit H-C et al (2020) Validation of a fully automated liver segmentation algorithm using multi-scale deep reinforcement learning and comparison versus manual segmentation. Eur J Radiol 126:108918PubMed
59.
Wang C, Song H, Chen L et al (2018) Automatic liver segmentation using multi-plane integrated fully convolutional neural networks2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp 1–6
60.
Tian J, Liu L, Shi Z, Xu F (2019) Automatic Couinaud segmentation from CT volumes on liver using GLC-UNet. In: Suk H-I, Liu M, Yan P, Lian C (eds) Machine Learning in Medical Imaging. Springer International Publishing, Cham, pp 274–282
61.
Tang M, Valipour S, Zhang Z, Cobzas D, Jagersand M (2017) A deep level set method for image segmentation. In: Cardoso MJ, Arbel T, Carneiro G et al (eds) Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support. Springer International Publishing, Cham, pp 126–134
62.
Seo H, Huang C, Bassenne M, Xiao R, Xing L (2020) 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 Imaging 39:1316–1325PubMed
63.
Selvi E, Selver MA, Güzeliş C, Dicle O (2014) A higher-order neural network design for improving segmentation performance in medical image series. J Phys: Conf Ser 490:012079
64.
Selvathi D, Malini C, Shanmugavalli P (2013) Automatic segmentation and classification of liver tumor in CT images using adaptive hybrid technique and Contourlet based ELM classifier2013 International Conference on Recent Trends in Information Technology (ICRTIT), pp 250–256
65.
Sayed GI, Hassanien AE, Schaefer G (2016) An automated computer-aided diagnosis system for abdominal CT liver images 20th conference on medical image understanding and analysis (MIUA 2016), Loughborough Univ, Loughborough, England, pp 68–73
66.
Sakboonyara B, Taeprasartsit P (2019) U-Net and mean-shift histogram for efficient liver segmentation from CT images2019 11th International Conference on Knowledge and Smart Technology (KST), pp 51–56
67.
K S, H LU, H KIM, S K, M T (2018) ROI-based fully automated liver registration in multi-phase CT Images2018 18th International Conference on Control, Automation and Systems (ICCAS), pp 645–649
68.
Raj A, Jayasree M (2016) Automated liver tumor detection using Markov random field segmentation International conference on emerging trends in engineering, science and technology (ICETEST - 2015), Trichur, India, pp 1305–1310
69.
Rafiei S, Nasr-Esfahani E, Najarian K, Karimi N, Samavi S, Soroushmehr SMR (2018) Liver segmentation in CT images using three dimensional to two dimensional fully convolutional network2018 25th IEEE International Conference on Image Processing (ICIP), pp 2067–2071
70.
Qin W, Wu J, Han F et al (2018) Superpixel-based and boundary-sensitive convolutional neural network for automated liver segmentation. Phys Med Biol 63:095017PubMedPubMedCentral
71.
Ponnoprat D, Inkeaw P, Chaijaruwanich J et al (2020) Classification of hepatocellular carcinoma and intrahepatic cholangiocarcinoma based on multi-phase CT scans. Med Biol Eng Comput 58:2497–2515PubMed
72.
Ouhmich F, Agnus V, Noblet V, Heitz F, Pessaux P (2019) Liver tissue segmentation in multiphase CT scans using cascaded convolutional neural networks. Int J Comput Assist Radiol Surg 14:1275–1284PubMed
73.
Ng YS, Xi Y, Qian Y et al (2020) Use of spectral detector computed tomography to improve liver segmentation and volumetry. J Comput Assist Tomogr 44:197–203PubMed
74.
Nayak A, Baidya Kayal E, Arya M et al (2019) Computer-aided diagnosis of cirrhosis and hepatocellular carcinoma using multi-phase abdomen CT. Int J Comput Assist Radiol Surg 14:1341–1352PubMed
75.
Mukherjee DP, Higashiura K, Okada T et al (2013) Utilizing disease-specific organ shape components for disease discrimination: application to discrimination of chronic liver disease from CT data16th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) pp 235-242. Nagoya Univ, Nagoya, Japan
76.
Morshid A, Elsayes KM, Khalaf AM et al (2019) A machine learning model to predict hepatocellular carcinoma response to transcatheter arterial chemoembolization. Radiol Artif Intell 1
77.
Mohagheghi S, Foruzan AH (2020) Incorporating prior shape knowledge via data-driven loss model to improve 3D liver segmentation in deep CNNs. Int J Comput Assist Radiol Surg 15:249–257PubMed
78.
Mofrad FB, Zoroofi RA, Tehrani-Fard AA, Akhlaghpoor S, Sato Y (2014) Classification of normal and diseased liver shapes based on Spherical Harmonics coefficients. J Med Syst 38:20PubMed
79.
Meng L, Tian Y, Bu S (2020) Liver tumor segmentation based on 3D convolutional neural network with dual scale. J Appl Clin Med Phys 21:144–157PubMed
80.
Luo S, Li J (2014) Accurate object segmentation using novel active shape and appearance models based on support vector machine learning2014 International Conference on Audio, Language and Image Processing, pp 347–351
81.
Lu F, Wu F, Hu P, Peng Z, Kong D (2017) Automatic 3D liver location and segmentation via convolutional neural network and graph cut. Int J Comput Assist Radiol Surg 12:171–182PubMed
82.
Selvaraj G, Janakiraman S (2013) Improved feature selection based on particle swarm optimization for liver disease diagnosis. In: 4th International Conference on Swarm, Evolutionary, and Memetic Computing (SEMCCO). Springer-Verlag Berlin, SRM University, Chennai, pp 214–225
83.
Li XH, Huang C, Jia FC, Li ZM, Fang CH, Fan YF (2014) Automatic liver segmentation using statistical prior models and free-form deformation. In: International Workshop on Medical Computer Vision - Algorithms for Big Data (MICCAI-bigMCV), Cambridge, pp 181–188
84.
Li X, Chen H, Qi X, Dou Q, Fu CW, Heng PA (2018) H-DenseUNet: hybrid densely connected unet for liver and tumor segmentation from CT volumes. IEEE Trans Med Imaging 37:2663–2674PubMed
85.
Liu Z, Song YQ, Sheng VS et al (2019) Liver CT sequence segmentation based with improved U-Net and graph cut. Expert Systems with Applications 126:54–63
86.
Linguraru MG, Richbourg WJ, Liu J et al (2012) Tumor burden analysis on computed tomography by automated liver and tumor segmentation. IEEE Trans Med Imaging 31:1965–1976PubMedPubMedCentral
87.
Afifi A, Nakaguchi T (2015) Unsupervised detection of liver lesions in CT images. In: 37th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC). IEEE, Milan, pp 2411–2414
88.
Roth K, Hesser J, Konopczynski T (2020) Mask mining for improved liver lesion segmentation. In: IEEE 17th International Symposium on Biomedical Imaging (ISBI). IEEE, Iowa, pp 943–947
89.
Tran ST, Cheng CH, Liu DG (2021) A multiple layer U-Net, U-n-Net, for liver and liver tumor segmentation in CT. IEEE Access 9:3752–3764
90.
Xu HL, Wang BH, Xue WG et al (2019) Automatic segmentation of liver CT image based on dense pyramid network. In: 1st International Workshop on Multiscale Multimodal Medical Imaging (MMMI). Springer International Publishing, Shenzhen, pp 10–16
91.
Yu AH, Liu Z, Sheng VS et al (2021) CT segmentation of liver and tumors fused multi-scale features. Intell Autom Soft Comput 30:589–599
92.
Zhang Y, Tian J, Zhong C et al (2021) DARN: Deep attentive refinement network for liver tumor segmentation from 3D CT volume. In: 25th International Conference on Pattern Recognition (ICPR). IEEE Computer Society, Electrical Network, pp 7796–7803
93.
Ayalew YA, Fante KA, Mohammed MA (2021) Modified U-Net for liver cancer segmentation from computed tomography images with a new class balancing method. BMC Biomedical Engineering 3:4PubMedPubMedCentral
94.
Chen WF, Ou HY, Liu KH et al (2021) In-series U-Net network to 3D tumor image reconstruction for liver hepatocellular carcinoma recognition. Diagnostics 11:18
95.
Chung M, Lee J, Park S, Lee CE, Lee J, Shin YG (2021) Liver segmentation in abdominal CT images via auto-context neural network and self-supervised contour attention*. Artif Intell Med 113:12
96.
Elmenabawy NA, Elnakib A, Moustafa HED (2020) Deep joint segmentation of liver and cancerous nodules from Ct images2020 37th National Radio Science Conference (NRSC), pp 296–301
97.
Fan TL, Wang GL, Li Y, Wang HR (2020) MA-Net: a multi-scale attention network for liver and tumor segmentation. IEEE Access 8:179656–179665
98.
He K, Liu XM, Shahzad R et al (2021) Advanced deep learning approach to automatically segment malignant tumors and ablation zone in the liver with contrast-enhanced CT. Front Oncol 11:10
99.
Kwon J, Choi K (2020) Trainable multi-contrast windowing for liver CT segmentation. In: IEEE International Conference on Big Data and Smart Computing (BigComp). IEEE, Busan, pp 169–172
100.
Lei T, Zhou WZ, Zhang YX et al (2020) Lightweight v-net for liver segmentation. In: 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing. IEEE, Barcelona, pp 1379–1383
101.
Ben-Cohen A, Klang E, Kerpel A, Konen E, Amitai MM, Greenspan H (2018) Fully convolutional network and sparsity-based dictionary learning for liver lesion detection in CT examinations. Neurocomputing 275:1585–1594
102.
Bevilacqua V, Carnimeo L, Brunetti A et al (2016) Synthesis of a neural network classifier for hepatocellular carcinoma grading based on triphasic CT images. In: 1st International Conference on Recent Trends in Image Processing and Pattern Recognition (RTIP2R). Springer-Verlag Berlin, Karnatak Arts Sci & Commerce Coll, Bidar, pp 356–368
103.
Chen L, Song H, Li Q, Cui YT, Yang J, Hu XHT (2019) Liver segmentation in CT images using a non-local fully convolutional neural network. In: IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, San Diego, pp 639–642
104.
Frid-Adar M, Diamant I, Klang E, Amitai M, Goldberger J, Greenspan H (2017) Modeling the intra-class variability for liver lesion detection using a multi-class patch-based CNN. In: 3rd International Workshop on Patch-Based Techniques in Medical Images (Patch-MI). Springer International Publishing Ag, Quebec City, pp 129–137
105.
Furuzuki M, Lu HM, Kim H et al (2019) A detection method for liver cancer region based on faster R-CNN. In: 19th International Conference on Control, Automation and Systems (ICCAS). IEEE, Jeju, pp 808–811
106.
Gong H, Yu LF, Leng S et al (2019) A deep learning- and partial least square regression-based model observer for a low-contrast lesion detection task in CT. Med Phys 46:2052–2063
107.
Huang WM, Li N, Lin ZP et al (2013) Liver tumor detection and segmentation using kernel-based extreme learning machine. In: 35th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC). IEEE, Osaka, pp 3662–3665
108.
Jin XY, Du ZH, Zhang T, Li LJ (2017) A disease detection method of liver based on improved convolutional neural network. In: 10th International Symposium on Computational Intelligence and Design (ISCID). IEEE, Hangzhou, pp 96–98
109.
Jin XY, Jin QL, Yang X (2015) A disease detection method of liver based on improved back propagation neural network. In: 8th International Symposium on Computational Intelligence and design (ISCID). IEEE, Hangzhou, pp 111–113
110.
Kim B, Kim J, Lee J-G, Kim DH, Park SH, Ye JC (2019) Unsupervised deformable image registration using cycle-consistent CNN. In: Shen D, Liu T, Peters TM et al (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. Springer International Publishing, Cham, pp 166–174
111.
Vivanti R, Szeskin A, Lev-Cohain N, Sosna J, Joskowicz L (2017) Automatic detection of new tumors and tumor burden evaluation in longitudinal liver CT scan studies. Int J Comput Assist Radiol Surg 12:1945–1957PubMed
112.
Tao QY, Ge ZY, Cai JF, Yin JX, See S (2019) Improving deep lesion detection using 3D contextual and spatial attention. In: 10th International Workshop on Machine Learning in Medical Imaging (MLMI) / 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). Springer International Publishing Ag, Shenzhen, pp 185–193
113.
Liang D, Lin L, Chen X et al (2019) Multi-stream scale-insensitive convolutional and recurrent neural networks for liver tumor detection in dynamic Ct Images2019 IEEE International Conference on Image Processing (ICIP), pp 794–798
114.
Lee S-g, Bae JS, Kim H, Kim JH, Yoon S (2018) Liver lesion detection from weakly-labeled multi-phase CT volumes with a grouped single shot MultiBox detector. In: Frangi AF, Schnabel JA, Davatzikos C, Alberola-López C, Fichtinger G (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2018. Springer International Publishing, Cham, pp 693–701
115.
Yang CJ, Wang CK, Fang YD et al (2021) Clinical application of mask region-based convolutional neural network for the automatic detection and segmentation of abnormal liver density based on hepatocellular carcinoma computed tomography datasets. PLoS ONE 16:e0255605PubMedPubMedCentral
116.
Zhou J, Gandomi AH, Chen F, Holzinger A (2021) Evaluating the quality of machine learning explanations: a survey on methods and metrics. Electronics 10:593
117.
Almotairi S, Kareem G, Aouf M, Almutairi B, Salem MA (2020) Liver tumor segmentation in CT scans using modified SegNet. Sensors (Basel) 20
118.
Anter AM, Hassenian AE (2019) CT liver tumor segmentation hybrid approach using neutrosophic sets, fast fuzzy c-means and adaptive watershed algorithm. Artif Intell Med 97:105–117PubMed
119.
Chen X, Lin LF, Liang D et al (2019) A dual-attention dilated residual network for liver lesion classification and localization on CT images. In: 26th IEEE International Conference on Image Processing (ICIP). IEEE, Taipei, pp 235–239
120.
Deng ZF, Guo QZ, Zhu ZL (2019) Dynamic regulation of level set parameters using 3D convolutional neural network for liver tumor segmentation. J Healthc Eng 2019:17
121.
Huang W, Yang Y, Lin Z et al (2014) Random feature subspace ensemble based extreme learning machine for liver tumor detection and segmentation2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp 4675–4678
122.
Kadoury S, Vorontsov E, Tang A (2015) Metastatic liver tumour segmentation from discriminant Grassmannian manifolds. Phys Med Biol 60:6459PubMed
123.
Zhou JY, Huang WM, Xiong W, Chen WY, Venkatesh SK (2013) Segmentation of hepatic tumor from abdominal CT data using an improved support vector machine framework. In: 35th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC). IEEE, Osaka, pp 3347–3350
124.
125.
Zhang Y, Jiang B, Wu J et al (2020) Deep learning initialized and gradient enhanced level-set based segmentation for liver tumor from CT images. IEEE Access 8:76056–76068
126.
Zhang X, Tian J, Xiang DH, Li XL, Deng KX (2011) Interactive liver tumor segmentation from CT scans using support vector classification with watershed. In: 33rd Annual International Conference of the IEEE Engineering-in-Medicine-and-Biology-Society (EMBS). IEEE, Boston, pp 6005–6008
127.
Wu Y, Zhou Q, Hu H, Rong G, Li Y, Wang S (2019) Hepatic lesion segmentation by combining plain and contrast-enhanced CT images with modality weighted U-Net2019 IEEE International Conference on Image Processing (ICIP), pp 255–259
128.
Wei Y, Jiang X, Liu K et al (2019) A hybrid multi-atrous and multi-scale network for liver lesion detection. In: Suk H-I, Liu M, Yan P, Lian C (eds) Machine Learning in Medical Imaging. Springer International Publishing, Cham, pp 364–372
129.
Vorontsov E, Tang A, Pal C, Kadoury S (2018) Liver lesion segmentation informed by joint liver segmentation2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp 1332–1335
130.
Vorontsov E, Tang A, Roy D, Pal CJ, Kadoury S (2017) Metastatic liver tumour segmentation with a neural network-guided 3D deformable model. Med Biol Eng Comput 55:127–139PubMed
131.
Todoroki Y, Iwamoto Y, Lin L, Hu H, Chen YW (2019) Automatic detection of focal liver lesions in multi-phase CT images using a multi-channel & multi-scale CNN2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp 872–875
132.
Sun C, Guo S, Zhang H, Li J, Ma S, Li X (2017) Liver lesion segmentation in CT images with MK-FCN2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), pp 1794–1798
133.
Shimizu A, Narihira T, Kobatake H, Furukawa D, Nawano S, Shinozaki K (2013) Ensemble learning based segmentation of metastatic liver tumours in contrast-enhanced computed tomography. IEICE Trans Inf Syst 96-D:864–868
134.
Moawad AW, Fuentes D, Khalaf AM et al (2020) Feasibility of automated volumetric assessment of large hepatocellular carcinomas’ responses to transarterial chemoembolization. Front Oncol 10:572PubMedPubMedCentral
135.
Radu C, Fisher P, Mitrea D et al (2020) Integration of real-time image fusion in the robotic-assisted treatment of hepatocellular carcinoma. Biol (Basel) 9
136.
Haq MNU, Irtaza A, Nida N, Shah MA, Zubair L (2021) Liver tumor segmentation using resnet based mask-R-CNN2021 International Bhurban Conference on Applied Sciences and Technologies (IBCAST), pp 276–281
137.
138.
Aslam MS, Younas M, Sarwar MU et al (2021) Liver-tumor detection using CNN ResUNet. Comput Mater Continua 67
139.
Dey R, Hong Y (2020) Hybrid cascaded neural network for liver lesion segmentation. In: IEEE 17th International Symposium on Biomedical Imaging (ISBI). IEEE, Iowa, pp 1173–1177
140.
Hamard A, Frandon J, Larbi A et al (2020) Impact of ultra-low dose CT acquisition on semi-automated RECIST tool in the evaluation of malignant focal liver lesions. Diagn Interv Imaging 101:473–479PubMed
141.
AmirHosseini B, Hosseini R (2019) An improved fuzzy-differential evolution approach applied to classification of tumors in liver CT scan images. Med Biol Eng Comput 57:2277–2287
142.
Balagourouchetty L, Pragatheeswaran JK, Pottakkat B, G R, (2020) GoogLeNet-based ensemble FCNet classifier for focal liver lesion diagnosis. IEEE J Biomed Health Inform 24:1686–1694PubMed
143.
Cao SE, Zhang LQ, Kuang SC et al (2020) Multiphase convolutional dense network for the classification of focal liver lesions on dynamic contrast-enhanced computed tomography. World J Gastroenterol 26:3660–3672PubMedPubMedCentral
144.
Das A, Acharya UR, Panda SS, Sabut S (2019) Deep learning based liver cancer detection using watershed transform and Gaussian mixture model techniques. Cogn Syst Res 54:165–175
145.
Devi RM, Seenivasagam V (2020) Automatic segmentation and classification of liver tumor from CT image using feature difference and SVM based classifier-soft computing technique. Soft Comput 24:18591–18598
146.
Jiang HY, Zheng RP, Yi DH, Zhao D (2013) A novel multiinstance learning approach for liver cancer recognition on abdominal CT images based on CPSO-SVM and IO. Comput Math Methods Med 2013:10
147.
Jin XY, Zhang T, Li LJ, Wu HT, Sun B (2016) Lesion recognition method of liver CT images based on random forest. In: 8th International Conference on Information Technology in Medicine and Education (ITME). IEEE, Fuzhou, pp 227–230
148.
Kabe GK, Song YQ, Liu Z (2020) Optimization of FireNet for liver lesion classification. Electronics 9:16
149.
Khalili K, Lawlor RL, Pourafkari M et al (2020) Convolutional neural networks versus radiologists in characterization of small hypoattenuating hepatic nodules on CT: a critical diagnostic challenge in staging of colorectal carcinoma. Sci Rep 10:10
150.
Kumar SS, Moni RS, Rajeesh J (2013) An automatic computer-aided diagnosis system for liver tumours on computed tomography images. Comput Electr Eng 39:1516–1526
151.
Kutlu H, Avci E (2019) A novel method for classifying liver and brain tumors using convolutional neural networks, discrete wavelet transform and long short-term memory networks. Sensors 19:16
152.
Yasaka K, Akai H, Abe O, Kiryu S (2018) Deep learning with convolutional neural network for differentiation of liver masses at dynamic contrast-enhanced CT: a preliminary study. Radiology 286:887–896PubMed
153.
Sreeja P, Hariharan S (2017) Image analysis for the detection and diagnosis of hepatocellular carcinoma from abdominal CT images. In: International Conference on Internet of Things for Technological Development (IoT4TD). Springer-Verlag Singapore Pte Ltd, Gandhinagar, pp 107–117
154.
Shi WQ, Kuang SC, Cao S et al (2020) Deep learning assisted differentiation of hepatocellular carcinoma from focal liver lesions: choice of four-phase and three-phase CT imaging protocol. Abdom Radiol (NY) 45:2688–2697
155.
Romero FP, Diler A, Bisson-Gregoire G et al (2019) End-to-end discriminative deep network for liver lesion classification. In: 16th IEEE International Symposium on Biomedical Imaging (ISBI). IEEE, Venice, pp 1243–1246
156.
Renukadevi T, Karunakaran S (2020) Optimizing deep belief network parameters using grasshopper algorithm for liver disease classification. Int J Imaging Syst Technol 30:168–184
157.
Rajathi GI, Jiji GW (2019) Chronic liver disease classification using hybrid whale optimization with simulated annealing and ensemble classifier. Symmetry-Basel 11:21
158.
Peng J, Kang S, Ning Z et al (2020) Residual convolutional neural network for predicting response of transarterial chemoembolization in hepatocellular carcinoma from CT imaging. Eur Radiol 30:413–424PubMed
159.
Özyurt F, Tuncer T, Avci E, Koç M, Serhatlioğlu İ (2019) A novel liver image classification method using perceptual hash-based convolutional neural network. Arab J Sci Eng 44:3173–3182
160.
Mala K, Sadasivam V, Alagappan S (2015) Neural network based texture analysis of CT images for fatty and cirrhosis liver classification. Appl Soft Comput 32:80–86
161.
Maaref A, Romero FP, Montagnon E et al (2020) Predicting the response to FOLFOX-based chemotherapy regimen from untreated liver metastases on baseline CT: a deep neural network approach. J Digit Imaging 33:937–945PubMedPubMedCentral
162.
Li J, Sun J, Shen NY, Chen EL, Zhang YC (2019) A CAD system for liver cancer diagnosis based on multi-phase CT images features with BP network. In: 11th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC). IEEE, Zhejiang University, Hangzhou, pp 67–70
163.
Liang D, Lin L, Hu H et al (2018) Combining convolutional and recurrent neural networks for classification of focal liver lesions in multi-phase CT images. In: Frangi AF, Schnabel JA, Davatzikos C, Alberola-López C, Fichtinger G (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2018. Springer International Publishing, Cham, pp 666–675
164.
Thuring J, Rippel O, Haarburger C et al (2020) Multiphase CT-based prediction of Child-Pugh classification: a machine learning approach. Eur Radiol Exp 4:9
165.
Wang MY, Fu FF, Zheng BJ et al (2021) Development of an AI system for accurately diagnose hepatocellular carcinoma from computed tomography imaging data. Br J Cancer 125:1111–1121
166.
Wang Q, Wang Z, Sun Y et al (2020) SCCNN: a diagnosis method for hepatocellular carcinoma and intrahepatic cholangiocarcinoma based on Siamese cross contrast neural network. IEEE Access 8:85271–85283
167.
Xu HY, Zou XH, Zhao YN et al (2021) Differentiation of intrahepatic cholangiocarcinoma and hepatic lymphoma based on radiomics and machine learning in contrast-enhanced computer tomography. Technol Cancer Res Treat 20:7
168.
Zhang J, Huang Z, Cao L et al (2020) Differentiation combined hepatocellular and cholangiocarcinoma from intrahepatic cholangiocarcinoma based on radiomics machine learning. Ann Transl Med 8:119PubMedPubMedCentral
169.
Giannini V, Rosati S, Defeudis A et al (2020) Radiomics predicts response of individual HER2-amplified colorectal cancer liver metastases in patients treated with HER2-targeted therapy. Int J Cancer 147:3215–3223PubMed
170.
Homayounieh F, Singh R, Nitiwarangkul C et al (2020) Semiautomatic segmentation and radiomics for dual-energy CT: A pilot study to differentiate benign and malignant hepatic lesions. AJR Am J Roentgenol 215:398–405
171.
Mao B, Zhang LZ, Ning PG et al (2020) Preoperative prediction for pathological grade of hepatocellular carcinoma via machine learning-based radiomics. Eur Radiol 30:6924–6932
172.
Mokrane FZ, Lu L, Vavasseur A et al (2020) Radiomics machine-learning signature for diagnosis of hepatocellular carcinoma in cirrhotic patients with indeterminate liver nodules. Eur Radiol 30:558–570PubMed
173.
Budai BK, Tóth A, Borsos P et al (2020) Three-dimensional CT texture analysis of anatomic liver segments can differentiate between low-grade and high-grade fibrosis. BMC Med Imaging 20:108PubMedPubMedCentral
174.
Huo Y, Terry JG, Wang J et al (2019) Fully automatic liver attenuation estimation combing CNN segmentation and morphological operations. Med Phys 46:3508–3519PubMedPubMedCentral
175.
Kayaaltı Ö, Aksebzeci BH, Karahan İÖ et al (2014) Liver fibrosis staging using CT image texture analysis and soft computing. Appl Soft Comput 25:399–413
176.
Yasaka K, Akai H, Kunimatsu A, Abe O, Kiryu S (2018) Deep learning for staging liver fibrosis on CT: a pilot study. Eur Radiol 28:4578–4585PubMed
177.
Son JH, Lee SS, Lee Y et al (2020) Assessment of liver fibrosis severity using computed tomography-based liver and spleen volumetric indices in patients with chronic liver disease. Eur Radiol 30:3486–3496PubMed
178.
Yin Y, Yakar D, Dierckx R, Mouridsen KB, Kwee TC, de Haas RJ (2021) Liver fibrosis staging by deep learning: a visual-based explanation of diagnostic decisions of the model. Eur Radiol 31:9620–9627PubMedPubMedCentral
179.
Ahmadi K, Karimi A, Fouladi Nia B (2016) New technique for automatic segmentation of blood vessels in CT scan images of liver based on optimized fuzzy c-means method. Comput Math Methods Med 2016:5237191PubMedPubMedCentral
180.
Ben-Cohen A, Klang E, Amitai MM, Goldberger J, Greenspan H (2018) Anatomical data augmentation for CNN based pixel-wise classification2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp 1096–1099
181.
Conze PH, Noblet V, Rousseau F et al (2017) Scale-adaptive supervoxel-based random forests for liver tumor segmentation in dynamic contrast-enhanced CT scans. Int J Comput Assist Radiol Surg 12:223–233
182.
Gensure RH, Foran DJ, Lee VM et al (2012) Evaluation of hepatic tumor response to yttrium-90 radioembolization therapy using texture signatures generated from contrast-enhanced CT images. Acad Radiol 19:1201–1207PubMedPubMedCentral
183.
Huang Q, Sun J, Ding H, Wang X, Wang G (2018) Robust liver vessel extraction using 3D U-Net with variant dice loss function. Comput Biol Med 101:153–162PubMed
184.
Zeng YZ, Zhao YQ, Liao M, Zou BJ, Wang XF, Wang W (2016) Liver vessel segmentation based on extreme learning machine. Phys Med 32:709–716PubMed
185.
Yu W, Fang B, Liu Y, Gao M, Zheng S, Wang Y (2019) Liver vessels segmentation based on 3d residual U-NET2019 IEEE International Conference on Image Processing (ICIP), pp 250–254
186.
Yang W, Lu Z, Yu M, Huang M, Feng Q, Chen W (2012) Content-based retrieval of focal liver lesions using bag-of-visual-words representations of single- and multiphase contrast-enhanced CT images. J Digit Imaging 25:708–719PubMedPubMedCentral
187.
Wang J, Han XH, Xu Y et al (2017) Sparse codebook model of local structures for retrieval of focal liver lesions using multiphase medical images. Int J Biomed Imaging 2017:1413297PubMedPubMedCentral
188.
Li Q, Yu B, Tian X, Cui X, Zhang R, Guo Q (2020) Deep residual nets model for staging liver fibrosis on plain CT images. Int J Comput Assist Radiol Surg 15:1399–1406PubMed
189.
Sun W, Qin N, Huang D, Liu Z, Ni S (2020) QN-S3VM method for evaluation of liver functional reserve2020 Chinese Automation Congress (CAC), pp 5629–5634
190.
Xu M, Wang Y, Chi Y, Hua X (2020) Training liver vessel segmentation deep neural networks on noisy labels from contrast CT imaging2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), pp 1552–1555
191.
Yang JZ, Fu MH, Hu Y (2021) Liver vessel segmentation based on inter-scale V-Net. Math Biosci Eng 18:4327–4340
192.
Yoshinobu Y, Iwamoto Y, Han XH et al (2020) Deep learning method for content-based retrieval of focal liver lesions using multiphase contrast-enhanced computer tomography images. In: IEEE International Conference on Consumer Electronics (ICCE). IEEE, Las Vegas, pp 598–601
193.
Gu J, Zhao Z, Zeng Z et al (2020) Multi-phase cross-modal learning for noninvasive gene mutation prediction in hepatocellular carcinoma42nd Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC), Montreal, Canada, pp 5814–5817
194.
Kobe A, Zgraggen J, Messmer F et al (2021) Prediction of treatment response to transarterial radioembolization of liver metastases: radiomics analysis of pre-treatment cone-beam CT: a proof of concept study. Eur J Radiol Open 8:100375PubMedPubMedCentral
195.
Li X, Qi Z, Du H et al (2022) Deep convolutional neural network for preoperative prediction of microvascular invasion and clinical outcomes in patients with HCCs. Eur Radiol 32:771–782PubMed
196.
Ahmad M, Ai DN, Xie GW et al (2019) Deep belief network modeling for automatic liver segmentation. IEEE Access 7:20585–20595
197.
Zhang Y, Peng C, Peng L et al (2022) DeepRecS: from RECIST diameters to precise liver tumor segmentation. IEEE J Biomed Health Inform 26:614–625PubMed
198.
Zhang Y, He Z, Zhong C, Zhang Y, Shi Z (2017) Fully convolutional neural network with post-processing methods for automatic liver segmentation from CT2017 Chinese Automation Congress (CAC), pp 3864–3869
199.
Bilic P, Christ P, Li HB et al (2023) The liver tumor segmentation benchmark (LiTS). Med Image Anal 84:102680PubMed
200.
Chen XY, Zhang R, Yang PK (2019) Feature fusion encoder decoder network for automatic liver lesion segmentation. In: 16th IEEE International Symposium on Biomedical Imaging (ISBI). IEEE, Venice, pp 430–433
201.
Vivanti R, Joskowicz L, Lev-Cohain N, Ephrat A, Sosna J (2018) Patient-specific and global convolutional neural networks for robust automatic liver tumor delineation in follow-up CT studies. Med Biol Eng Comput 56:1699–1713PubMed
202.
Zhou J, Wang W, Lei B et al (2020) Automatic detection and classification of focal liver lesions based on deep convolutional neural networks: a preliminary study. Front Oncol 10:581210PubMed
203.
Adcock A, Rubin D, Carlsson G (2014) Classification of hepatic lesions using the matching metric. Comput Vis Image Underst 121:36–42
204.
Liang D, Lin LF, Hu HJ et al (2018) Residual convolutional neural networks with global and local pathways for classification of focal liver lesions. In: 15th Pacific Rim International Conference on Artificial Intelligence (PRICAI) / 15th Pacific Rim Knowledge Acquisition Workshop (PKAW). Springer International Publishing Ag, Nanjing, pp 617–628
205.
Wang W, Chen Q, Iwamoto Y et al (2020) Deep fusion models of multi-phase CT and selected clinical data for preoperative prediction of early recurrence in hepatocellular carcinoma. IEEE Access 8:139212–139220
206.
Zhang L, Xia W, Yan ZP et al (2020) Deep learning predicts overall survival of patients with unresectable hepatocellular carcinoma treated by transarterial chemoembolization plus sorafenib. Front Oncol 10:593292PubMedPubMedCentral
207.
Wang J, Han XH, Xu Y et al (2017) Tensor sparse representation of temporal features for content-based retrieval of focal liver lesions using multi-phase medical images2017 IEEE International Symposium on Multimedia (ISM), pp 507–510
208.
Group TFMCS, Bedossa P (1994) Intraobserver and interobserver variations in liver biopsy interpretation in patients with chronic hepatitis C. Hepatology 20:15–20
209.
Sterling RK, Lissen E, Clumeck N et al (2006) Development of a simple noninvasive index to predict significant fibrosis in patients with HIV/HCV coinfection. Hepatology 43:1317–1325PubMed
210.
Metadata
Title
Performance and clinical applicability of machine learning in liver computed tomography imaging: a systematic review
Authors
Keyur Radiya
Henrik Lykke Joakimsen
Karl Øyvind Mikalsen
Eirik Kjus Aahlin
Rolv-Ole Lindsetmo
Kim Erlend Mortensen
Publication date
12-05-2023
Publisher
Springer Berlin Heidelberg
Published in
European Radiology / Issue 10/2023
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI
https://doi.org/10.1007/s00330-023-09609-w

Other articles of this Issue 10/2023

European Radiology 10/2023 Go to the issue

Ultrasound

Analysis of Sonazoid contrast-enhanced ultrasound for predicting the risk of microvascular invasion in hepatocellular carcinoma: a prospective multicenter study

Cardiac

Analysis of bi-atrial function using CMR feature tracking and long-axis shortening approaches in patients with diastolic dysfunction and atrial fibrillation

Imaging Informatics and Artificial Intelligence

Artificial intelligence–based prediction of cervical lymph node metastasis in papillary thyroid cancer with CT

Magnetic Resonance

Prediction of Ki-67 labeling index, ATRX mutation, and MGMT promoter methylation status in IDH-mutant astrocytoma by morphological MRI, SWI, DWI, and DSC-PWI

Imaging Informatics and Artificial Intelligence

An artificial intelligence framework for the diagnosis of prosthetic joint infection based on 99mTc-MDP dynamic bone scintigraphy

Magnetic Resonance

Improved visualization of hepatic tumors in magnetic resonance–guided thermoablation using T1-inversion-recovery imaging with variable inversion time