@book{Lamport:Book:1989,
 author = {Lamport, Leslie},
 title = {Latex: A Document Preparation System},
 year = {1986},
 isbn = {0-201-15790-X},
 publisher = {Addison-Wesley Longman Publishing Co., Inc.},
 address = {Boston, MA, USA},
}

@misc{Hinton:arXiv:2015:Distilling,
 title={Distilling the knowledge in a neural network},
 author={Hinton, Geoffrey and Vinyals, Oriol and Dean, Jeff},
 OPTeprinttype={arXiv},
 OPTeprint={1503.02531},
 howpublished={Eprint \href{http://arxiv.org/abs/1503.02531}{arXiv:1503.02531}},
 OPTurl={http://arxiv.org/abs/1503.02531},
 year=2015
}

@article{Ludwig2021,
author = {Ludwig, Daniel R. and Fraum, Tyler J. and Ballard, David H. and Narra, Vamsi R. and Shetty, Anup S.},
title = {Imaging Biomarkers of Hepatic Fibrosis: Reliability and Accuracy of Hepatic Periportal Space Widening and Other Morphologic Features on MRI},
journal = {American Journal of Roentgenology},
volume = {216},
number = {5},
pages = {1229-1239},
year = {2021},
doi = {10.2214/AJR.20.23099},
    note ={PMID: 33729883}
}

@article{Severino2020,
	author = "P. Severino and A. D'Amato and M. Pucci and F. Infusino and F. Adamo and L. I. Birtolo and L. Netti and G. Montefusco and C. Chimenti and C. Lavalle and V. Maestrini and M. Mancone and W. M. Chilian and F. Fedele",
	title = "Ischemic Heart Disease Pathophysiology Paradigms Overview: From Plaque Activation to Microvascular Dysfunction",
	journal = "International Journal of Molecular Sciences",
	volume = "21",
	number = "21",
	pages = "8118",
	year = "2020",
	doi = "10.3390/ijms21218118"
}

@misc{CDC2024,
    author = "{Centers for Disease Control and Prevention}",
    title = "{Chronic Liver Disease}",
    year = "2024",
    month = apr,
    url = "https://www.cdc.gov/nchs/fastats/liver-disease.htm",
    note = "Accessed: 2024-08-05"
}

@misc{NIDDKCirrhosis,
    author = "{National Institute of Diabetes and Digestive and Kidney Diseases}",
    title = "{Cirrhosis - NIDDK}",
    url = "https://www.niddk.nih.gov/health-information/liver-disease/cirrhosis",
    note = "Accessed: 2024-08-05"
}

@misc{NIDDKSymptoms2023,
    author = "{National Institute of Diabetes and Digestive and Kidney Diseases}",
    title = "{Symptoms \& Causes of Cirrhosis - NIDDK}",
    year = "2023",
    month = jun,
    url = "https://www.niddk.nih.gov/health-information/liver-disease/cirrhosis/symptoms-causes",
    note = "Accessed: 2024-08-05"
}

@misc{ALF_Fibrosis2022,
    author = "{American Liver Foundation}",
    title = "{Fibrosis: Development}",
    year = "2022",
    month = jun,
    url = "https://liverfoundation.org/about-your-liver/how-liver-diseases-progress/fibrosis-scarring/",
    note = "Accessed: 2024-08-05"
}

@article{Yin2021,
    author = "Y. Yin and D. Yakar and R. A. J. O. Dierckx and K. B. Mouridsen and T. C. Kwee and R. J. de Haas",
    title = "{Liver fibrosis staging by deep learning: a visual-based explanation of diagnostic decisions of the model}",
    journal = "European Radiology",
    volume = "31",
    number = "12",
    pages = "9620--9627",
    year = "2021",
    doi = "10.1007/s00330-021-08046-x"
}

@article{Lee2022,
    author = "S. Lee and D. C. Elton and A. H. Yang and others",
    title = "{Fully Automated and Explainable Liver Segmental Volume Ratio and Spleen Segmentation at CT for Diagnosing Cirrhosis}",
    journal = "Radiology: Artificial Intelligence",
    volume = "4",
    number = "5",
    pages = "e210268",
    year = "2022",
    doi = "10.1148/ryai.210268"
}

@article{Pickhardt2016,
    author = "P. J. Pickhardt and K. Malecki and J. Kloke and M. G. Lubner",
    title = "{Accuracy of Liver Surface Nodularity Quantification on MDCT as a Noninvasive Biomarker for Staging Hepatic Fibrosis}",
    journal = "American Journal of Roentgenology",
    volume = "207",
    number = "6",
    pages = "1194--1199",
    year = "2016",
    doi = "10.2214/AJR.16.16514"
}

@article{Im2022,
    author = "W.H. Im and J.S. Song and W. Jang",
    title = "{Noninvasive staging of liver fibrosis: review of current quantitative CT and MRI-based techniques}",
    journal = "Abdominal Radiology",
    volume = "47",
    number = "",
    pages = "3051--3067",
    year = "2022",
    doi = "10.1007/s00261-021-03181-x"
}

@article{Mazumder2023,
    author = "N R. Mazumder and B. Enchakalody and P. Zhang and G.L. Su",
    title = "{Using Artificial Intelligence to Predict Cirrhosis From Computed Tomography Scans}",
    journal = "Clinical and Translational Gastroenterology",
    volume = "14",
    number = "10",
    pages = "",
    year = "2023",
    doi = "10.14309/ctg.0000000000000616"
}

@article{Sartoris2018,
author = {Sartoris, Riccardo and Rautou, Pierre-Emmanuel and Elkrief, Laure and Pollorsi, Gaia and Durand, Fran\c{c}ois and Valla, Dominique and Spahr, Laurent and Terraz, Sylvain and Soubrane, Olivier and Cauchy, Fran\c{c}ois and Vilgrain, Val\'{e}rie and Ronot, Maxime},
title = {Quantification of Liver Surface Nodularity at CT: Utility for Detection of Portal Hypertension},
journal = {Radiology},
volume = {289},
number = {3},
pages = {698-707},
year = {2018},
doi = {10.1148/radiol.2018181131},
note ={PMID: 30179109},
URL = {https://doi.org/10.1148/radiol.2018181131},
eprint = {https://doi.org/10.1148/radiol.2018181131}
}

@article{Pickhardt2017,
    author = "P. J. Pickhardt and K. Malecki and O.F. Hunt, C. Beaumont and J. Kloke and T.J. Ziemlewicz and M. G. Lubner",
    title = "{Hepatosplenic volumetric assessment at MDCT for staging liver fibrosis}",
    journal = "European Radiology",
    volume = "27",
    number = "",
    pages = "3060--3068",
    year = "2017",
    doi = "10.1007/s00330-016-4648-0"
}

@article{Kim2012,
    author = "I. Kim and Y.J. Jang and H. Ryeom and S.M. Lee and H.J. Lee and G.C. Kim and H.J. Kim",
    title = "{Variation in Hepatic Segmental Volume Distribution According to Different Causes of Liver Cirrhosis: CT Volumetric Evaluation}",
    journal = "J. Computer Assisted Tomography",
    volume = "36",
    number = "2",
    pages = "220--225",
    year = "2012",
    doi = "10.1097/RCT.0b013e31824afd86"
}

@article{Ozaki2016,
    author = {Ozaki, Kumi and Matsui, Osamu and Kobayashi, Satoshi and Minami, Tetsuya and Kitao, Azusa and Gabata, Toshifumi},
    title = "{Morphometric changes in liver cirrhosis: aetiological differences correlated with progression}",
    journal = {British Journal of Radiology},
    volume = {89},
    number = {1059},
    pages = {20150896},
    year = {2016},
    month = {02},
    abstract = "{To evaluate the morphometric changes in liver cirrhosis using multidetector CT volumetry and to analyse the differences in morphometric changes among different aetiologies and stages of cirrhosis.Each portal segment with the respective proportion relative to total liver volume was measured in 54 patients without cirrhosis as a control (male/female, 29/25; 62.4 ± 7.6 years) and 250 patients with cirrhosis (male/female, 172/78; 64.6 ± 9.2 years) related to hepatitis virus infection (n = 96), alcoholism (n = 88) and non-alcoholic steatohepatitis (NASH) (n = 66). 149 patients were classified as patients with Child–Pugh Class A, 57 patients as patients with Class B and 44 patients as patients with Class C. The Kruskal–Wallis test was used for statistical analysis (p \\&lt; 0.05).Cirrhosis associated with all aetiologies commonly showed atrophy of the medial and anterior segments and right lobe and hypertrophy of the lateral segment and caudate lobe compared with the control (p \\&lt; 0.05). In Child–Pugh Class A, hypertrophy of the caudate lobe progressed more in alcoholism and NASH than in virus-related aetiologies (p \\&lt; 0.001). Hypertrophy of the lateral segment and atrophy of the medial and anterior segments and right lobe progressed less in NASH than in cases with virus related and alcoholic cirrhosis (p \\&lt; 0.001). In patients with Class B, these differences were less prominent than in those with Class A (p \\&lt; 0.001). In Class C, no significant differences were noted in any segment, regardless of aetiology (p \\&gt; 0.05).Morphometric changes of cirrhosis display different patterns according to aetiology. Differences between aetiologies would decrease with progression of cirrhosis.Morphometric changes of cirrhosis display different patterns according to aetiology. Differences between aetiologies would decrease with progression of cirrhosis.}",
    issn = {0007-1285},
    doi = {10.1259/bjr.20150896},
    url = {https://doi.org/10.1259/bjr.20150896},
    eprint = {https://academic.oup.com/bjr/article-pdf/89/1059/20150896/54454596/bjr.20150896.pdf},
}


@article{Zhong2019,
    author = "H.J Zhong and H.H. Sun and L.F. Xue and E.M. McGowan and Y. Chen",
    title = "{Differential hepatic features presenting in Wilson disease-associated cirrhosis and hepatitis B-associated cirrhosis}",
    journal = "World J Gastroenterology",
    volume = "25",
    number = "3",
    pages = "378--387",
    year = "2019",
    doi = "10.3748/wjg.v25.i3.378"
}

@article{Zhang2009,
author = {Zhang, Yang and Zhang, Xiao Ming and Prowda, Joan C. and Zhang, Hong Lei and Sant'Anna Henry, Carolina and Shih, George and Emond, Jean C. and Prince, Martin R.},
title = {Changes in hepatic venous morphology with cirrhosis on MRI},
journal = {Journal of Magnetic Resonance Imaging},
volume = {29},
number = {5},
pages = {1085-1092},
keywords = {MRI, cirrhosis, hepatic vein, liver transplantation, gadolinium, MRA},
doi = {https://doi.org/10.1002/jmri.21718},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/jmri.21718},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/jmri.21718},
abstract = {Abstract Purpose To identify changes in vascular morphology on magnetic resonance imaging (MRI) in patients with cirrhosis and to compare these findings to liver donors. Materials and Methods Patients undergoing liver transplantation with biopsy-proven cirrhosis (n = 74) and liver donor candidates (n = 85) underwent dynamic gadolinium-enhanced 3D MR at 1.5T. Vessel diameters were measured independently by three radiologists and features of cirrhosis were identified and correlated with cirrhosis. Results Hepatic veins were smaller in patients with cirrhosis (4.9, 4.5, and 5.0 mm for right, middle, and left vs. 9.9, 7.6, and 8.9 mm in donors, P ≪ 0.001) and were negatively correlated with cirrhosis (P < 0.001). Right hepatic vein (RHV) <5 mm diagnosed cirrhosis with 59\% sensitivity and 99\% specificity; the sensitivity and specificity were 88\% and 85\% for RHV <7 mm. Main portal vein was minimally larger in cirrhosis, 14 versus 12 mm (P < 0.001) in donors. Right portal veins were smaller in cirrhotic patients, 6.5 and 6.2 mm compared to 8.4 and 7.6 mm (P ≪ 0.001), respectively, in donors. Conclusion Vascular features of cirrhosis include small hepatic veins, minimally enlarged main portal vein, and small intrahepatic portal veins; these features may facilitate identification of cirrhosis. J. Magn. Reson. Imaging 2009;29:1085–1092. © 2009 Wiley-Liss, Inc.},
year = {2009}
}

@article{VanBeers2001,
author = {Van Beers, Bernard E. and Leconte, Isabelle and Materne, Roland and Smith, Anne M. and Jamart, Jacques and Horsmans, Yves},
title = {Hepatic Perfusion Parameters in Chronic Liver Disease},
journal = {American Journal of Roentgenology},
volume = {176},
number = {3},
pages = {667-673},
year = {2001},
doi = {10.2214/ajr.176.3.1760667},
    note ={PMID: 11222202},

URL = { 
    
        https://doi.org/10.2214/ajr.176.3.1760667
    
    

},
eprint = { 
    
        https://doi.org/10.2214/ajr.176.3.1760667
    
    

}
,
    abstract = { OBJECTIVE. The aim of our study was to determine if hepatic perfusion parameters measured with CT change in relation to disease severity in patients with chronic liver disease.SUBJECTS AND METHODS. Dynamic contrast-enhanced single-section CT scans of the liver were obtained in 40 individuals who included six control subjects, 16 patients with noncirrhotic chronic liver disease, and 18 patients with cirrhosis. Hepatic, aortic, and portal venous time—density curves were fitted to a dual-input one-compartment model to calculate the liver perfusion, arterial fraction, distribution volume, and mean transit time.RESULTS. Liver perfusion decreased in patients with cirrhosis (67 ± 23 mL · min-1 · 100 mL-1 versus 108 ± 34 mL · min-1 · 100 mL-1 in control subjects [p = 0.009] and 98 ± 36 mL · min-1 · 100 mL-1 in patients with noncirrhotic chronic liver disease [p = 0.003]), and the arterial fraction and the mean transit time increased (41 ± 27\% and 51 ± 79 sec versus 17 ± 16\% and 16 ± 5 sec in control subjects, and 19 ± 6\% and 17 ± 8 sec in patients with noncirrhotic chronic liver disease [p < 0.05]). A significant correlation was seen between these three perfusion parameters and the severity of chronic liver disease based on clinical and biologic data (p < 0.001). No significant change in distribution volume was observed.CONCLUSION. Hepatic perfusion parameters measured with CT were significantly altered in cirrhosis and correlated with the severity of chronic liver disease. }
}

@article{Choi2018,
author = {Choi, Kyu                         Jin and Jang, Jong                         Keon and Lee, Seung                         Soo and Sung, Yu                         Sub and Shim, Woo                         Hyun and Kim, Ho                         Sung and Yun, Jessica and Choi, Jin-Young and Lee, Yedaun and Kang, Bo-Kyeong and Kim, Jin                         Hee and Kim, So                         Yeon and Yu, Eun                         Sil},
title = {Development and Validation of a Deep Learning System for Staging                     Liver Fibrosis by Using Contrast Agent–enhanced CT Images in the                     Liver},
journal = {Radiology},
volume = {289},
number = {3},
pages = {688-697},
year = {2018},
doi = {10.1148/radiol.2018180763},
    note ={PMID: 30179104},

URL = { 
    
        https://doi.org/10.1148/radiol.2018180763
    
    

},
eprint = { 
    
        https://doi.org/10.1148/radiol.2018180763
    
    

}
,
    abstract = { Purpose To develop and validate a deep learning system (DLS) for staging liver fibrosis by using CT images in the liver. Materials and Methods DLS for CT-based staging of liver fibrosis was created by using a development data set that included portal venous phase CT images in 7461 patients with pathologically confirmed liver fibrosis. The diagnostic performance of the DLS was evaluated in separate test data sets for 891 patients. The influence of patient characteristics and CT techniques on the staging accuracy of the DLS was evaluated by logistic regression analysis. In a subset of 421 patients, the diagnostic performance of the DLS was compared with that of the radiologist’s assessment, aminotransferase-to-platelet ratio index (APRI), and fibrosis-4 index by using the area under the receiver operating characteristic curve (AUROC) and Obuchowski index. Results In the test data sets, the DLS had a staging accuracy of 79.4\% (707 of 891) and an AUROC of 0.96, 0.97, and 0.95 for diagnosing significant fibrosis (F2–4), advanced fibrosis (F3–4), and cirrhosis (F4), respectively. At multivariable analysis, only pathologic fibrosis stage significantly affected the staging accuracy of the DLS (P = .016 and .013 for F1 and F2, respectively, compared with F4), whereas etiology of liver disease and CT technique did not. The DLS (Obuchowski index, 0.94) outperformed the radiologist’s interpretation, APRI, and fibrosis-4 index (Obuchowski index range, 0.71–0.81; P ˂ .001) for staging liver fibrosis. Conclusion The deep learning system allows for accurate staging of liver fibrosis by using CT images. © RSNA, 2018 Online supplemental material is available for this article. }
}



@article{Diaz-Soto2022,
author = {Maria P. Diaz-Soto and Guadalupe Garcia-Tsao},
title ={Management of varices and variceal hemorrhage in liver cirrhosis: a recent update},

journal = {Therapeutic Advances in Gastroenterology},
volume = {15},
number = {},
pages = {17562848221101712},
year = {2022},
doi = {10.1177/17562848221101712},
    note ={PMID: 35757384},

URL = { 
    
        https://doi.org/10.1177/17562848221101712
    
    

},
eprint = { 
    
        https://doi.org/10.1177/17562848221101712
    
    

}
,
    abstract = { Cirrhosis consists of two main stages: compensated (asymptomatic) and decompensated, the latter with a higher mortality. Variceal hemorrhage, together with ascites or encephalopathy, or both, are events that define cirrhosis decompensation and are driven by portal hypertension. The approach and management of patients with compensated cirrhosis has been mostly focused on preventing variceal hemorrhage in those who have high-risk varices on endoscopy. Recent studies suggest a paradigm shift aimed at preventing all decompensating events, not only variceal hemorrhage, in patients with cirrhosis and clinically significant portal hypertension identified via noninvasive measures such as liver stiffness and platelet count. In these patients, nonselective beta-blockers have been shown to prevent ascites (the most common decompensating event) and variceal growth. Variceal hemorrhage has a high mortality rate and even though advances in diagnostic approach and standard of care over the past decades have led to a decrease in mortality, it is still high with a 6-week mortality rate of 15–20\%. Survival has improved with the preemptive placement of the transjugular intrahepatic portosystemic shunt in patients at high risk of failing standard therapy. In this review, we provide an overview of the pathophysiology and bases for therapy of portal hypertension and varices, the diagnostic approach and management of compensated cirrhosis with clinically significant portal hypertension, and the management of acute variceal hemorrhage as well as prevention strategies for variceal hemorrhage recurrence. }
}




@article{Mathai2024_ARAD,
    author = "Mathai, Tejas S. and Lubner, Meghan G. and Pickhardt, Perry J. and Summers, Ronald M.",
    title = "{Fully Automated and Explainable Measurement of Liver Surface Nodularity in CT: Utility for Staging Hepatic Fibrosis }",
    journal = "Academic Radiology",
    volume = "1",
    number = "1",
    pages = "1--10",
    year = "2024"    
}


@article{Mathai2023,
    author = "Mathai, Tejas S. and others",
    title = "{Liver Surface Nodularity for Staging Hepatic Fibrosis on CT:
A Comparative Study of Liver Segmenters}",
    journal = "Scientific Abstracts from the 2024 Conference on Machine Intelligence in Medical Imaging (CMIMI) of the Society for Imaging Informatics in Medicine (SIIM), Journal of Imaging Informatics in Medicine (Supplement)",
    volume = "37",
    number = "1",
    pages = "1--35",
    year = "2024"
}

@article{Lewis2024_CMIMI,
    author = "Lewis, Sydney V. and Mathai, Tejas S. and Lubner, Meghan G. and Pickhardt, Perry J. and Summers, Ronald M.",
    title = "{Utility of Fully Automated Liver and Spleen Biomarkers for Staging Hepatic Fibrosis in CT}",
    journal = "Scientific Abstracts from the 2024 Conference on Machine Intelligence in Medical Imaging (CMIMI) of the Society for Imaging Informatics in Medicine (SIIM), Journal of Imaging Informatics in Medicine (Supplement)",
    volume = "37",
    number = "1",
    pages = "1--35",
    year = "2024"
}

@inproceedings{liu2024self,
  title={{Self and Mixed Supervision to Improve Training Labels for Multi-Class Medical Image Segmentation}},
  author={Liu, Jianfei and Parnell, Christopher and Summers, Ronald M},
  booktitle={Medical Imaging 2024: Computer-Aided Diagnosis},
  volume={12927},
  pages={1292704},
  year={2024},
  organization={SPIE}
}

@article{wasserthal2023totalsegmentator,
  title={TotalSegmentator: robust segmentation of 104 anatomic structures in CT images},
  author={Wasserthal, Jakob and Breit, Hanns-Christian and Meyer, Manfred T and Pradella, Maurice and Hinck, Daniel and Sauter, Alexander W and Heye, Tobias and Boll, Daniel T and Cyriac, Joshy and Yang, Shan and others},
  journal={Radiology: Artificial Intelligence},
  volume={5},
  number={5},
  year={2023},
  publisher={Radiological Society of North America}
}

@article{Antonelli2022,
   author = {Antonelli, M. and Reinke, A. and others},
   title = {The Medical Segmentation Decathlon},
   journal = {Nat Commun},
   volume = {13},
   number = {1},
   pages = {4128},
   year = {2022}
}

@article{isensee2021nnu,
  title={nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation},
  author={Isensee, Fabian and Jaeger, Paul F and Kohl, Simon AA and Petersen, Jens and Maier-Hein, Klaus H},
  journal={Nature methods},
  volume={18},
  number={2},
  pages={203--211},
  year={2021},
  publisher={Nature Publishing Group}
}


@article{Yu2023,
	author = {Yu, Xinming and Botezatu, Simona and Tzolos, Evangelos and Dey, Damini and Kwiecinski, Jacek},
	title = {Pericoronary adipose tissue CT attenuation in coronary artery plaque inflammation},
	volume = {109},
	number = {6},
	pages = {485--493},
	year = {2023},
	doi = {10.1136/heartjnl-2022-321158},
	publisher = {BMJ Publishing Group Ltd},
	issn = {1355-6037},
	URL = {https://heart.bmj.com/content/109/6/485},
	eprint = {https://heart.bmj.com/content/109/6/485.full.pdf},
	journal = {Heart}
}

@INPROCEEDINGS{Nguyen2024,
  author={Nguyen, Andrew M. and Sudharshan Mathai, Tejas and Liu, Liangchen and Liu, Jianfei and Summers, Ronald M.},
  booktitle={2024 IEEE International Symposium on Biomedical Imaging (ISBI)}, 
  title={Automated Measurement of Pericoronary Adipose Tissue Attenuation and Volume in Ct Angiography}, 
  year={2024},
  volume={},
  number={},
  pages={1-5},
  keywords={Three-dimensional displays;Volume measurement;Angiography;Biomedical measurement;Attenuation measurement;Attenuation;Fats},
  doi={10.1109/ISBI56570.2024.10635570}}

@article{Chatterjee2022,
title = {Perivascular fat attenuation for predicting adverse cardiac events in stable patients undergoing invasive coronary angiography},
journal = {Journal of Cardiovascular Computed Tomography},
volume = {16},
number = {6},
pages = {483-490},
year = {2022},
issn = {1934-5925},
doi = {https://doi.org/10.1016/j.jcct.2022.05.004},
url = {https://www.sciencedirect.com/science/article/pii/S1934592522000727},
author = {Devina Chatterjee and Benjamin L. Shou and Matthew B. Matheson and Mohammad R. Ostovaneh and Carlos Rochitte and Marcus Y. Chen and Marc Dewey and Jason Ortman and Christopher Cox and Joao A.C. Lima and Armin Arbab-Zadeh},
keywords = {Multidetector computed tomography, Perivascular fat attenuation, Coronary artery disease, Coronary computed tomography angiography, Coronary heart disease},
abstract = {Background
Inflammation surrounding the coronary arteries can be non-invasively assessed using pericoronary adipose tissue attenuation (PCAT). While PCAT holds promise for further risk stratification of patients with low coronary artery disease (CAD) prevalence, its value in higher risk populations remains unknown.
Methods
CORE320 enrolled patients referred for invasive coronary angiography with known or suspected CAD. Coronary computed tomography angiography (CCTA) images were collected for 381 patients for whom clinical outcomes were assessed 5 years after enrollment. Using semi-automated image analysis software, PCAT was obtained and normalized for the right coronary (RCA), left anterior descending (LAD), and left circumflex arteries (LCx). The association between PCAT and major adverse cardiovascular events (MACE) during follow up was assessed using Cox regression models.
Results
Thirty-seven patients were excluded due to technical failure. For the remaining 344 patients, median age was 62 (interquartile range, 55–68) with 59% having ≥1 coronary artery stenosis of ≥50% by quantitative coronary angiography. Mean attenuation values for PCAT in RCA, LAD, and LCx were −74.9, −74.2, and −71.2, respectively. Hazard ratios and 95% confidence intervals (CI) for normalized PCAT in the RCA, LAD, and LCx for MACE were 0.96 (CI: 0.75–1.22, p ​= ​0.71), 1.31 (95% CI: 0.96–1.78, p ​= ​0.09), and 0.98 (95% CI: 0.78–1.22, p ​= ​0.84), respectively. For death, stroke, or myocardial infarction only, hazard ratios were 0.68 (0.44–1.07), 0.85 (0.56–1.29), and 0.57 (0.41–0.80), respectively.
Conclusions
In patients referred for invasive coronary angiography with suspected CAD, PCAT did not predict MACE during long term follow up. Further studies are needed to understand the relationship of PCAT with CAD risk.}
}

@article{Yen2018,
    doi = {10.1371/journal.pone.0199760},
    author = {Yen, Yi-Hao AND Kuo, Fang-Ying AND Kee, Kwong-Ming AND Chang, Kuo-Chin AND Tsai, Ming-Chao AND Hu, Tsung-Hui AND Lu, Sheng-Nan AND Wang, Jing-Houng AND Hung, Chao-Hung AND Chen, Chien-Hung},
    journal = {PLOS ONE},
    publisher = {Public Library of Science},
    title = {APRI and FIB-4 in the evaluation of liver fibrosis in chronic hepatitis C patients stratified by AST level},
    year = {2018},
    month = {06},
    volume = {13},
    url = {https://doi.org/10.1371/journal.pone.0199760},
    pages = {1-16},
    abstract = {Background and aim The aspartate aminotransferase (AST)-to-platelet ratio index (APRI) and fibrosis-4 index (FIB-4) are commonly used compound surrogates for advanced fibrosis in chronic hepatitis C (CHC) patients. However, the use of APRI and FIB-4 entails a risk of overestimating the fibrosis stage due to the impact of necroinflammatory activity on transaminases. We sought to investigate the optimal cutoff values of the two compound surrogates for predicting cirrhosis stratified by AST level.   Methods This retrospective study enrolled 1716 treatment-naive CHC patients who underwent liver biopsy prior to interferon therapy from 1997–2010. Fibrosis was scored according to the modified Knodell classification. The upper limit for normal AST in our hospital is 37 IU/L. We stratified the enrolled patients into the categories of AST≤37 IU/L (N = 132), 37<AST≤74, (N = 501), 74<AST≤148 IU/L (N = 737), and AST>148 IU/L (N = 346).   Results 436 patients had cirrhosis (F4). The area under receiver operating characteristic (AUROC) analysis results distinguishing cirrhosis (F4) from non-cirrhosis (F0–F3) were 0.81 for APRI and 0.85 for FIB-4 in patients with AST≤37 IU/L; 0.71 for APRI and 0.72 for FIB-4 in patients with 37<AST≤74IU/L; 0.72 for APRI and 0.73 for FIB-4 in patients with 74<AST≤148 IU/L; and 0.68 for APRI and 0.70 for FIB-4 in patients with AST>148 IU/L. The optimal cutoff values of APRI and FIB-4 for the diagnosis of cirrhosis were 0.6 and 1.4, respectively, in patients with AST≤37 IU/L; 1.1 and 2.2, respectively, in patients with 37<AST≤74 IU/L; 2.2 and 3.4, respectively, in patients with 74<AST≤148 IU/L; and 3.4 and 5.5, respectively, in patients with AST>148 IU/L.   Conclusions We provide optimal cutoff values of both APRI and FIB-4 to predict cirrhosis stratified by AST levels, which should be more feasible compared with the single cutoff values proposed in previous studies.},
    number = {6},

}

@article{Ma2020,
   author = {Ma, Runlei and Ties, Daan and van Assen, Marly and Pelgrim, Gert Jan and Sidorenkov, Grigory and van Ooijen, Peter M. A. and van der Harst, Pim and van Dijk, Randy and Vliegenthart, Rozemarijn},
   title = {Towards reference values of pericoronary adipose tissue attenuation: impact of coronary artery and tube voltage in coronary computed tomography angiography},
   journal = {European Radiology},
   volume = {30},
   number = {12},
   pages = {6838-6846},
   abstract = {Objectives To determine normal pericoronary adipose tissue mean attenuation (PCATMA) values for left the anterior descending (LAD), left circumflex (LCX), and right coronary artery (RCA) in patients without plaques on coronary CT angiography (cCTA), taking into account tube voltage influence. Methods This retrospective study included 192 patients (76 (39.6%) men; median age 49 years (range, 19–79)) who underwent cCTA with third-generation dual-source CT for the suspicion of CAD between 2015 and 2017. We selected patients without plaque on cCTA. PCATMA was measured semi-automatically on cCTA images in the proximal segment of the three main coronary arteries with 10 mm length. Paired t-testing was used to compare PCATMA between combinations of two coronary arteries within each patient, and one-way ANOVA testing was used to compare PCATMA in different kV groups. Results The overall mean ± standard deviation (SD) PCATMA was − 90.3 ± 11.1 HU. PCATMA in men was higher than that in women: − 88.5 ± 10.5 HU versus − 91.5 ± 11.3 HU (p = 0.001). PCATMA of LAD, LCX, and RCA was − 92.4 ± 11.6 HU, − 88.4 ± 9.9 HU, and − 90.2 ± 11.4 HU, respectively. Pairwise comparison of the arteries showed significant difference in PCATMA: LAD and LCX (p < 0.001), LAD and RCA (p = 0.009), LCX and RCA (p = 0.033). PCATMA of the 70 kV, 80 kV, 90 kV, 100 kV, and 120 kV groups was − 95.6 ± 9.6 HU, − 90.2 ± 11.5 HU, − 87.3 ± 9.9 HU, − 82.7 ± 6.2 HU, and − 79.3 ± 6.8 HU, respectively (p < 0.001). Conclusions In patients without plaque on cCTA, PCATMA varied by tube voltage, with minor differences in PCATMA between coronary arteries (LAD, LCX, RCA). PCATMA values need to be interpreted taking into account tube voltage setting. Key Points • In patients without plaque on cCTA, PCATMA differs slightly by coronary artery (LAD, LCX, RCA)., • Tube voltage of cCTA affects PCATMA measurement, with mean PCATMA increasing linearly with increasing kV., • For longitudinal cCTA analysis of PCATMA , the use of equal kV setting is strongly recommended. Electronic supplementary material The online version of this article (10.1007/s00330-020-07069-0) contains supplementary material, which is available to authorized users.},
   ISSN = {0938-7994},
   DOI = {10.1007/s00330-020-07069-0},
   url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7599179/},
   year = {2020},
   type = {Journal Article}
}

@article{Simpson2017_CRLM,
author = {Simpson, Amber L and Doussot, Alexandre and Creasy, John M and Adams, Lauryn B and Allen, Peter J and DeMatteo, Ronald P and Gönen, Mithat and Kemeny, Nancy E and Kingham, T Peter and Shia, Jinru and Jarnagin, William R and Do, Richard K G and D'Angelica, Michael I},
address = {Baltimore, MD :},
issn = {1068-9265},
journal = {Annals of surgical oncology.},
lccn = {2001214677},
number = {9},
publisher = {Lippincott Williams & Wilkins},
title = {Computed Tomography Image Texture: A Noninvasive Prognostic Marker of Hepatic Recurrence After Hepatectomy for Metastatic Colorectal Cancer.},
volume = {24},
year = {20179},
}

@article{Zheng2017_MSD,
title = {Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma Using Quantitative Image Analysis},
journal = {Journal of the American College of Surgeons},
volume = {225},
number = {6},
pages = {778-788.e1},
year = {2017},
issn = {1072-7515},
doi = {https://doi.org/10.1016/j.jamcollsurg.2017.09.003},
url = {https://www.sciencedirect.com/science/article/pii/S1072751517319658},
author = {Jian Zheng and Jayasree Chakraborty and William C. Chapman and Scott Gerst and Mithat Gonen and Linda M. Pak and William R. Jarnagin and Ronald P. DeMatteo and Richard K.G. Do and Amber L. Simpson and Peter J. Allen and Vinod P. Balachandran and Michael I. D'Angelica and T Peter Kingham and Neeta Vachharajani},
abstract = {Background
Microvascular invasion (MVI) is a significant risk factor for early recurrence after resection or transplantation for hepatocellular carcinoma (HCC). Knowledge of MVI status would help guide treatment recommendations, but is generally identified after operation. This study aims to predict MVI preoperatively using quantitative image analysis.
Study Design
One hundred and twenty patients from 2 institutions underwent resection of HCC from 2003 to 2015 were included. The largest tumor from preoperative CT was subjected to quantitative image analysis, which uses an automated computer algorithm to capture regional variation in CT enhancement patterns. Quantitative imaging features by automatic analysis, qualitative radiographic descriptors by 2 radiologists, and preoperative clinical variables were included in multivariate analysis to predict histologic MVI.
Results
Histologic MVI was identified in 19 (37%) patients with tumors ≤5 cm and 34 (49%) patients with tumors >5 cm. Among patients with tumors ≤5 cm, none of the clinical findings or radiographic descriptors were associated with MVI; however, quantitative features based on angle co-occurrence matrix predicted MVI with an area under curve of 0.80, positive predictive value of 63%, and negative predictive value of 85%. In patients with tumors >5 cm, higher α-fetoprotein level, larger tumor size, and viral hepatitis history were associated with MVI, and radiographic descriptors were not. However, a multivariate model combining α-fetoprotein, tumor size, hepatitis status, and quantitative feature based on local binary pattern predicted MVI with area under curve of 0.88, positive predictive value of 72%, and negative predictive value of 96%.
Conclusions
This study reveals the potential importance of quantitative image analysis as a predictor of MVI.}
}

@article{Pak2018_MSD,
title = {Quantitative Imaging Features and Postoperative Hepatic Insufficiency: A Multi-Institutional Expanded Cohort},
journal = {Journal of the American College of Surgeons},
volume = {226},
number = {5},
pages = {835-843},
year = {2018},
issn = {1072-7515},
doi = {https://doi.org/10.1016/j.jamcollsurg.2018.02.001},
url = {https://www.sciencedirect.com/science/article/pii/S1072751518301224},
author = {Linda M. Pak and Jayasree Chakraborty and Mithat Gonen and William C. Chapman and Richard K.G. Do and Bas {Groot Koerkamp} and Kees Verhoef and Ser Yee Lee and Marco Massani and Eric P. {van der Stok} and Amber L. Simpson and William R. Jarnagin and Peter J. Allen and Michael I. D'Angelica and Ronald P. DeMatteo and T. Peter Kingham and Vinod P. Balachandran},
abstract = {Background
Post-hepatectomy liver insufficiency (PHLI) is a significant cause of morbidity and mortality after liver resection. Quantitative imaging analysis using CT scans measures variations in pixel intensity related to perfusion. A preliminary study demonstrated a correlation between quantitative imaging features of the future liver remnant (FLR) parenchyma from preoperative CT scans and PHLI. The objective of this study was to explore the potential application of quantitative imaging analysis in PHLI in an expanded, multi-institutional cohort.
Study Design
We retrospectively identified patients from 5 high-volume academic centers who developed PHLI after major hepatectomy, and matched them to control patients without PHLI (by extent of resection, preoperative chemotherapy treatment, age [±5 years], and sex). Quantitative imaging features were extracted from the FLR in the preoperative CT scan, and the most discriminatory features were identified using conditional logistic regression. Percent remnant liver volume (RLV) was defined as follows: (FLR volume)/(total liver volume) × 100. Significant clinical and imaging features were combined in a multivariate analysis using conditional logistic regression.
Results
From 2000 to 2015, 74 patients with PHLI and 74 matched controls were identified. The most common indications for surgery were colorectal liver metastases (53%), hepatocellular carcinoma (37%), and cholangiocarcinoma (9%). Two CT imaging features (FD1_4: image complexity; ACM1_10: spatial distribution of pixel intensity) were strongly associated with PHLI and remained associated with PHLI on multivariate analysis (p = 0.018 and p = 0.023, respectively), independent of clinical variables, including preoperative bilirubin and %RLV.
Conclusions
Quantitative imaging features are independently associated with PHLI and are a promising preoperative risk stratification tool.}
}

@article{Isensee2024,
  title={nnU-Net Revisited: A Call for Rigorous Validation in 3D Medical Image Segmentation},
  author={Fabian Isensee and others},
  journal={ArXiv},
  year={2024},
  volume={abs/2404.09556},
  url={https://api.semanticscholar.org/CorpusID:269148560}
}

@incollection{Ziessman2006,
title = {Chapter 7 - Hepatobiliary System},
editor = {Harvey A. Ziessman and Janis P. O'Malley and James H. Thrall},
booktitle = {Nuclear Medicine (Third Edition)},
publisher = {Mosby},
edition = {Third Edition},
address = {Philadelphia},
pages = {159-214},
year = {2006},
series = {The Requisites in Radiology},
isbn = {978-0-323-02946-9},
doi = {https://doi.org/10.1016/B978-0-323-02946-9.50012-X},
url = {https://www.sciencedirect.com/science/article/pii/B978032302946950012X},
author = {Harvey A. Ziessman and Janis P. O'Malley and James H. Thrall}
}

@article{Tallam2022,
author = {Tallam, Hima and others},
title = "{Fully Automated Abdominal {CT} Biomarkers for Type 2 Diabetes Using                     Deep Learning}",
journal = {Radiology},
volume = {304},
number = {1},
pages = {85-95},
year = {2022},
doi = {10.1148/radiol.211914},
    note ={PMID: 35380492},

URL = { 
    
        https://doi.org/10.1148/radiol.211914
    
    

},
eprint = { 
    
        https://doi.org/10.1148/radiol.211914
    
    

}

    
}

@article{Karcaaltincaba2007,
title = {Multidetector CT and MRI findings in periportal space pathologies},
journal = {European Journal of Radiology},
volume = {61},
number = {1},
pages = {3-10},
year = {2007},
note = {Diffuse Liver Diseases},
issn = {0720-048X},
doi = {https://doi.org/10.1016/j.ejrad.2006.11.009},
url = {https://www.sciencedirect.com/science/article/pii/S0720048X06004499},
author = {Musturay Karcaaltincaba and Mithat Haliloglu and Erhan Akpinar and Deniz Akata and Mustafa Ozmen and Macit Ariyurek and Okan Akhan},
keywords = {Periportal, Pathology, Hepatic artery, Portal vein, Periportal halo, Nerves, Lymphatics, Periportal space},
abstract = {Periportal region is an anatomic space around portal vein comprising hepatic artery, bile duct, nerves, lymphatics and a potential space. Periportal pathologies may involve any of these structures diffusely or focally with characteristic radiologic findings. Radiologic findings can be helpful in differential diagnosis of pathologies of periportal structures including periportal cavernomatous transformation, hepatic artery aneurysm, biliary diseases, neurofibromatosis, lymphoma, langerhans’ cell histiocytosis, periportal fatty infiltration and other causes of periportal halo in adult and pediatric patients. Lobar/segmental intrahepatic involvement can be seen in neurofibromatosis, cavernomatous transformation, fatty infiltration and periportal edema. In this review, we discuss CT and MRI findings of periportal pathologies which can be in the form of diffuse or segmental/lobar involvement.}
}

@article{Kahn2022,
    author = {Kahn, Darcy E. and Bergman, Bryan C.},
    title = {Keeping It Local in Metabolic Disease: Adipose Tissue Paracrine Signaling and Insulin Resistance},
    journal = {Diabetes},
    volume = {71},
    number = {4},
    pages = {599-609},
    year = {2022},
    month = {03},
    abstract = {Alterations in adipose tissue composition and function are associated with obesity and contribute to the development of type 2 diabetes. While the significance of this relationship has been cemented, our understanding of the multifaceted role of adipose tissue in metabolic heath and disease continues to evolve and expand. Heterogenous populations of cells that make up adipose tissue throughout the body generate diverse secretomes containing a mosaic of bioactive compounds with vast structural and signaling capabilities. While there are many reports highlighting the important role of adipose tissue endocrine signaling in insulin resistance and type 2 diabetes, the direct, local, paracrine effect of adipose tissue has received less attention. Recent studies have begun to underscore the importance of considering anatomically discrete adipose depots for their specific impact on local microenvironments and metabolic function in neighboring tissues as well as regulation of whole-body physiology. This article highlights the important role of adipose tissue paracrine signaling on metabolic function and insulin sensitivity in nearby tissues and organs, specifically focusing on visceral, pancreatic, subcutaneous, intermuscular, and perivascular adipose tissue depots.},
    issn = {0012-1797},
    doi = {10.2337/dbi21-0020},
    url = {https://doi.org/10.2337/dbi21-0020},
    eprint = {https://diabetesjournals.org/diabetes/article-pdf/71/4/599/671756/dbi210020.pdf},
}



@article{Lawrence2012,
author = {Lawrence, David A. and Oliva, Isabel B. and Israel, Gary M.},
title = {Detection of Hepatic Steatosis on Contrast-Enhanced CT Images: Diagnostic Accuracy of Identification of Areas of Presumed Focal Fatty Sparing},
journal = {American Journal of Roentgenology},
volume = {199},
number = {1},
pages = {44-47},
year = {2012},
doi = {10.2214/AJR.11.7838},
    note ={PMID: 22733892},

URL = { 
    
        https://doi.org/10.2214/AJR.11.7838
    
    

},
eprint = { 
    
        https://doi.org/10.2214/AJR.11.7838
    
    

}
,
    abstract = { OBJECTIVE. The purpose of this article is to determine the diagnostic accuracy of identifying focal areas of increased density along the gallbladder fossa or in the periphery of segment IV for diagnosing hepatic steatosis. MATERIALS AND METHODS. Five hundred consecutive three-phase CT examinations were retrospectively evaluated. Two reference standards for hepatic steatosis were determined using the unenhanced CT examination: a liver-spleen attenuation difference of greater than 10 HU and the absolute attenuation of the liver less than 40 HU. The portal venous phase was independently analyzed by two radiologists. Hepatic steatosis was diagnosed on the contrast-enhanced images if there was increased attenuation in the liver, either at the gallbladder fossa or in the posterior medial aspect of segment IV, when compared with background liver parenchyma. RESULTS. The criterion of relative liver-spleen attenuation difference diagnosed 38 cases. The criterion of absolute liver attenuation less than 40 HU diagnosed 44 cases. Of these cases, hepatic steatosis was diagnosed on the portal venous phase in 23 cases (κ = 1.0), with no false-positive cases. The criterion of relative liver-spleen attenuation difference yielded sensitivity, specificity, positive predictive value, and negative predictive value of 60.5\%, 100\%, 100\%, and 96.9\%, respectively. The criterion of absolute liver attenuation less than 40 HU yielded sensitivity, specificity, positive predictive value, and negative predictive value of 52.5\%, 100\%, 100\%, and 95.7\%, respectively. CONCLUSION. Qualitative evaluation of the liver on a portal venous phase contrast-enhanced CT is highly specific for the diagnosis of hepatic steatosis; the sensitivity of the method, however, is rather low. }
}

@article{Lastra2015,
url = {https://doi.org/10.1515/hmbci-2015-0010},
title = {Perivascular adipose tissue, inflammation and insulin resistance: link to vascular dysfunction and cardiovascular disease},
title = {},
author = {Guido Lastra and Camila Manrique},
pages = {19--26},
volume = {22},
number = {1},
journal = {Hormone Molecular Biology and Clinical Investigation},
doi = {doi:10.1515/hmbci-2015-0010},
year = {2015},
lastchecked = {2025-01-31}
}


@Article{Valentini2023,
AUTHOR = {Valentini, Alessia and Cardillo, Carmine and Della Morte, David and Tesauro, Manfredi},
TITLE = {The Role of Perivascular Adipose Tissue in the Pathogenesis of Endothelial Dysfunction in Cardiovascular Diseases and Type 2 Diabetes Mellitus},
JOURNAL = {Biomedicines},
VOLUME = {11},
YEAR = {2023},
NUMBER = {11},
ARTICLE-NUMBER = {3006},
URL = {https://www.mdpi.com/2227-9059/11/11/3006},
PubMedID = {38002006},
ISSN = {2227-9059},
ABSTRACT = {Cardiovascular diseases (CVDs) and type 2 diabetes mellitus (T2DM) are two of the four major chronic non-communicable diseases (NCDs) representing the leading cause of death worldwide. Several studies demonstrate that endothelial dysfunction (ED) plays a central role in the pathogenesis of these chronic diseases. Although it is well known that systemic chronic inflammation and oxidative stress are primarily involved in the development of ED, recent studies have shown that perivascular adipose tissue (PVAT) is implicated in its pathogenesis, also contributing to the progression of atherosclerosis and to insulin resistance (IR). In this review, we describe the relationship between PVAT and ED, and we also analyse the role of PVAT in the pathogenesis of CVDs and T2DM, further assessing its potential therapeutic target with the aim of restoring normal ED and reducing global cardiovascular risk.},
DOI = {10.3390/biomedicines11113006}
}

@article{Song2024,
author = {Bin Song and Yali Qu and Jie Chen and Jie Yang and Hua-Lin Yan and Han-Yu Jiang and Bin Song},
title = {Noninvasive diagnosis of liver cirrhosis: qualitative and quantitative imaging biomarkers},
journal = {Abdominal Radiology},
year = {2024},
publisher = {Springer Nature},
month = {feb},
url = {https://doi.org/10.1007/s00261-024-04225-8},
doi = {10.1007/s00261-024-04225-8}
}

@article{Ito2000,
author = {Ito, Katsuyoshi and Mitchell, Donald G. and Gabata, Toshifumi},
title = {Enlargement of hilar periportal space: A sign of early cirrhosis at MR imaging},
journal = {Journal of Magnetic Resonance Imaging},
volume = {11},
number = {2},
pages = {136-140},
keywords = {liver, MR, cirrhosis, liver, diseases, hepatitis},
doi = {https://doi.org/10.1002/(SICI)1522-2586(200002)11:2<136::AID-JMRI9>3.0.CO;2-B},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/%28SICI%291522-2586%28200002%2911%3A2%3C136%3A%3AAID-JMRI9%3E3.0.CO%3B2-B},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/%28SICI%291522-2586%28200002%2911%3A2%3C136%3A%3AAID-JMRI9%3E3.0.CO%3B2-B},
abstract = {Abstract The purpose of this study was to determine whether the finding of an enlarged hilar periportal space is a sign for early cirrhosis at magnetic resonance (MR) imaging. Forty-one pathologically proved cirrhotic patients in the early stage of disease who did not show conventional imaging findings of cirrhosis (early cirrhosis group) and 47 patients without history of chronic liver diseases (control group) were included in this study. MR images were qualitatively and quantitatively evaluated for the presence of enlargement of the periportal space. Enlargement of the periportal space was seen in 98\% of patients in the early cirrhosis group, while this finding was seen in 11\% of patients in the control group (P < 0.0001). The mean value of the hilar periportal fat thickness was significantly greater (P < 0.0001) in the early cirrhosis group (15.5 ± 6.2 mm) than in the control group (5.3 ± 3.1 mm). The sensitivity, specificity, accuracy, and positive predictive value of this finding for the MR diagnosis of cirrhosis with a cutoff value of 10 mm were 93\%, 92\%, 92\%, and 91\%, respectively. Enlargement of the hilar periportal space is a helpful sign at MR imaging in the discrimination between normal and early cirrhotic livers. J. Magn. Reson. Imaging 2000;11:136–140. © 2000 Wiley-Liss, Inc.},
year = {2000}
}

@article{Li2024,
author = {Li, Shengwei and Li, Xiao-Guang and Zhou, Fanyu and Zhang, Yumeng and Bie, Zhixin and Cheng, Lin and Peng, Jinzhao and Li, Bin},
title = {Automated segmentation of liver and hepatic vessels on portal venous phase computed tomography images using a deep learning algorithm},
journal = {Journal of Applied Clinical Medical Physics},
volume = {25},
number = {8},
pages = {e14397},
keywords = {computed tomography, deep learning, liver, segmentation},
doi = {https://doi.org/10.1002/acm2.14397},
url = {https://aapm.onlinelibrary.wiley.com/doi/abs/10.1002/acm2.14397},
eprint = {https://aapm.onlinelibrary.wiley.com/doi/pdf/10.1002/acm2.14397},
abstract = {Abstract Background CT-image segmentation for liver and hepatic vessels can facilitate liver surgical planning. However, time-consuming process and inter-observer variations of manual segmentation have limited wider application in clinical practice. Purpose Our study aimed to propose an automated deep learning (DL) segmentation algorithm for liver and hepatic vessels on portal venous phase CT images. Methods This retrospective study was performed to develop a coarse-to-fine DL-based algorithm that was trained, validated, and tested using private 413, 52, and 50 portal venous phase CT images, respectively. Additionally, the performance of the DL algorithm was extensively evaluated and compared with manual segmentation using an independent clinical dataset of preoperative contrast-enhanced CT images from 44 patients with hepatic focal lesions. The accuracy of DL-based segmentation was quantitatively evaluated using the Dice Similarity Coefficient (DSC) and complementary metrics [Normalized Surface Dice (NSD) and Hausdorff distance\_95 (HD95) for liver segmentation, Recall and Precision for hepatic vessel segmentation]. The processing time for DL and manual segmentation was also compared. Results Our DL algorithm achieved accurate liver segmentation with DSC of 0.98, NSD of 0.92, and HD95 of 1.52 mm. DL-segmentation of hepatic veins, portal veins, and inferior vena cava attained DSC of 0.86, 0.89, and 0.94, respectively. Compared with the manual approach, the DL algorithm significantly outperformed with better segmentation results for both liver and hepatic vessels, with higher accuracy of liver and hepatic vessel segmentation (all p < 0.001) in independent 44 clinical data. In addition, the DL method significantly reduced the manual processing time of clinical postprocessing (p < 0.001). Conclusions The proposed DL algorithm potentially enabled accurate and rapid segmentation for liver and hepatic vessels using portal venous phase contrast CT images.},
year = {2024}
}

@article{Ibragimov2017,
doi = {10.1088/1361-6560/aa9262},
url = {https://dx.doi.org/10.1088/1361-6560/aa9262},
year = {2017},
month = {nov},
publisher = {IOP Publishing},
volume = {62},
number = {23},
pages = {8943},
author = {Ibragimov, Bulat and Toesca, Diego and Chang, Daniel and Koong, Albert and Xing, Lei},
title = {Combining deep learning with anatomical analysis for segmentation of the portal vein for liver SBRT planning},
journal = {Physics in Medicine \& Biology}
}
