{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T05:30:01Z","timestamp":1775280601931,"version":"3.50.1"},"reference-count":118,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2019,8,19]],"date-time":"2019-08-19T00:00:00Z","timestamp":1566172800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2019,8,19]],"date-time":"2019-08-19T00:00:00Z","timestamp":1566172800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"name":"ukm","award":["GUP-2014-066"],"award-info":[{"award-number":["GUP-2014-066"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Digit Imaging"],"published-print":{"date-parts":[[2020,4]]},"DOI":"10.1007\/s10278-019-00262-8","type":"journal-article","created":{"date-parts":[[2019,8,19]],"date-time":"2019-08-19T18:02:36Z","timestamp":1566237756000},"page":"304-323","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Survey on Liver Tumour Resection Planning System: Steps, Techniques, and Parameters"],"prefix":"10.1007","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4978-5350","authenticated-orcid":false,"given":"Omar Ibrahim","family":"Alirr","sequence":"first","affiliation":[]},{"given":"Ashrani Aizzuddin Abd.","family":"Rahni","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,8,19]]},"reference":[{"key":"262_CR1","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1159\/000018770","volume":"16","author":"C Couinaud","year":"1999","unstructured":"Couinaud C: Liver anatomy: portal and suprahepatic or biliary segmentation. Dig Surg 16:459\u2013467, 1999","journal-title":"Dig Surg"},{"key":"262_CR2","doi-asserted-by":"crossref","first-page":"23","DOI":"10.5923\/j.ajbe.20120202.05","volume":"2","author":"AH Foruzan","year":"2012","unstructured":"Foruzan AH, Chen Y-W, Zoroofi RA, Kaibori M: Analysis of CT Images of Liver for Surgical Planning. Am J Biomed Eng 2:23\u201328, 2012","journal-title":"Am J Biomed Eng"},{"key":"262_CR3","doi-asserted-by":"crossref","first-page":"577","DOI":"10.1097\/RCT.0b013e31828f0baa","volume":"37","author":"T Zahel","year":"2013","unstructured":"Zahel T et al.: Rapid assessment of liver volumetry by a novel automated segmentation algorithm. J Comput Assist Tomogr 37:577\u2013582, 2013","journal-title":"J Comput Assist Tomogr"},{"key":"262_CR4","doi-asserted-by":"crossref","unstructured":"El Khodary M, Milot L, Reinhold C: Imaging of Hepatic Metastases. In Liver Metastasis: Biology and Clinical Management 307\u2013351, 2011","DOI":"10.1007\/978-94-007-0292-9_11"},{"key":"262_CR5","first-page":"335","volume":"107","author":"UP Neumann","year":"2010","unstructured":"Neumann UP, Seehofer D, Neuhaus P: The surgical treatment of hepatic metastases in colorectal carcinoma. Dtsch Arztebl Int 107:335\u2013342, 2010","journal-title":"Dtsch Arztebl Int"},{"key":"262_CR6","doi-asserted-by":"crossref","first-page":"2725","DOI":"10.3748\/wjg.v22.i9.2725","volume":"22","author":"C Hackl","year":"2016","unstructured":"Hackl C, Schlitt HJ, Renner P, Lang SA: Liver surgery in cirrhosis and portal hypertension. World J Gastroenterol 22:2725\u20132735, 2016","journal-title":"World J Gastroenterol"},{"key":"262_CR7","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1055\/s-2008-1076796","volume":"133","author":"R Grundmann","year":"2008","unstructured":"Grundmann R et al.: Diagnostik und Therapie von Lebermetastasen kolorektaler Karzinome - Workflow. Zentralbl Chir 133:267\u2013284, 2008","journal-title":"Zentralbl Chir"},{"key":"262_CR8","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1007\/978-1-4939-2326-7_16","volume-title":"Imaging and Visualization in The Modern Operating Room","author":"MA Scherer","year":"2015","unstructured":"Scherer MA, Geller DA: New Preoperative Images, Surgical Planning, and Navigation. In: Imaging and Visualization in The Modern Operating Room. New York: Springer, 2015, pp. 205\u2013214"},{"key":"262_CR9","unstructured":"Coulon P, De Brouwer F, Steinberg A: Clinical uses for CT Liver Analysis application, 2013"},{"key":"262_CR10","doi-asserted-by":"crossref","first-page":"629","DOI":"10.1001\/archsurg.140.7.629","volume":"140","author":"H Lang","year":"2005","unstructured":"Lang H et al.: Impact of Virtual Tumor Resection and Computer-Assisted Risk Analysis on Operation Planning and Intraoperative Strategy in Major Hepatic Resection. Arch Surg 140:629, 2005","journal-title":"Arch Surg"},{"key":"262_CR11","unstructured":"Fong JS, Ibrahim H: Development of a virtual reality system for Hepatocellular Carcinoma pre-surgical planning. in 2010 2nd International Conference on Software Technology and Engineering IEEE, 1, 2010, pp 1\u201341"},{"issue":"2","key":"262_CR12","first-page":"173","volume":"78","author":"H Meinzer","year":"2004","unstructured":"Meinzer H et al.: Computer-based Surgery Planning For Living Liver Donation. Transplantation 78(2):173\u2013174, 2004","journal-title":"Transplantation"},{"key":"262_CR13","doi-asserted-by":"crossref","first-page":"1344","DOI":"10.1109\/TMI.2002.801166","volume":"21","author":"D Selle","year":"2002","unstructured":"Selle D, Preim B, Schenk A, Peitgen HO: Analysis of vasculature for liver surgical planning. IEEE Trans Med Imaging 21:1344\u20131357, 2002","journal-title":"IEEE Trans Med Imaging"},{"key":"262_CR14","doi-asserted-by":"crossref","unstructured":"Shevchenko N, Seidl B, Schwaiger J, Markert M, Lueth TC: MiMed liver: A planning system for liver surgery. 2010 Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBC\u201910 1882\u20131885, 2010","DOI":"10.1109\/IEMBS.2010.5627120"},{"key":"262_CR15","unstructured":"American Cancer Society. Facts & Figures. Atlanta, Ga, 2019"},{"key":"262_CR16","first-page":"307","volume-title":"Imaging of Hepatic Metastases","author":"M El Khodary","year":"2011","unstructured":"El Khodary M, Milot L, Reinhold C: Imaging of Hepatic Metastases. Dordrecht: Springer, 2011, pp. 307\u2013351"},{"key":"262_CR17","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1148\/radiol.2312021639","volume":"231","author":"P Soyer","year":"2004","unstructured":"Soyer P et al.: Detection of hypovascular hepatic metastases at triple-phase helical CT: sensitivity of phases and comparison with surgical and histopathologic findings. Radiology 231:413\u2013420, 2004","journal-title":"Radiology"},{"key":"262_CR18","doi-asserted-by":"crossref","first-page":"872","DOI":"10.1148\/radiol.2353041099","volume":"235","author":"HI Khalil","year":"2005","unstructured":"Khalil HI, Patterson SA, Panicek DM: Hepatic lesions deemed too small to characterize at CT: prevalence and importance in women with breast cancer. Radiology 235:872\u2013878, 2005","journal-title":"Radiology"},{"issue":"3","key":"262_CR19","doi-asserted-by":"crossref","first-page":"810","DOI":"10.1148\/radiol.2323030896","volume":"232","author":"D Sahani","year":"2004","unstructured":"Sahani D, Kalva S, Tanabe K, Hayat S: Intraoperative US in patients undergoing surgery for liver neoplasms: comparison with MR imaging 1. Radiology 232(3):810\u2013814, 2004","journal-title":"Radiology"},{"key":"262_CR20","unstructured":"Zaheer S: Fast segmentation of vessels in MR liver images using patient specific models. PhD diss., 2013"},{"key":"262_CR21","unstructured":"Assistant Radiology. Liver - Incidentalomas. \nhttp:\/\/www.radiologyassistant.nl\/2017\n\n. Accessed 12 May 2007"},{"key":"262_CR22","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1007\/978-3-642-28557-8_24","volume-title":"Abdominal Imaging. Computational and Clinical Applications","author":"V Pamulapati","year":"2012","unstructured":"Pamulapati V, Venkatesan A, Wood BJ, Linguraru MG: Liver segmental anatomy and analysis from vessel and tumor segmentation via optimized graph cuts. In: Abdominal Imaging. Computational and Clinical Applications. Berlin: Springer, 2012, pp. 189\u2013197"},{"key":"262_CR23","doi-asserted-by":"crossref","first-page":"771","DOI":"10.1118\/1.3284530","volume":"37","author":"MG Linguraru","year":"2010","unstructured":"Linguraru MG, Sandberg JK, Li Z, Shah F, Summers RM: Automated segmentation and quantification of liver and spleen from CT images using normalized probabilistic atlases and enhancement estimation. Med Phys 37:771\u2013783, 2010","journal-title":"Med Phys"},{"key":"262_CR24","doi-asserted-by":"crossref","first-page":"229","DOI":"10.2217\/iim.12.13","volume":"4","author":"FE Boas","year":"2012","unstructured":"Boas FE, Fleischmann D: CT artifacts: causes and reduction techniques. Imaging Med 4:229\u2013240, 2012","journal-title":"Imaging Med"},{"key":"262_CR25","doi-asserted-by":"crossref","first-page":"520","DOI":"10.1007\/978-3-540-73400-0_66","volume-title":"Applications of Fuzzy Sets Theory","author":"P Campadelli","year":"2007","unstructured":"Campadelli P, Casiraghi E: Liver segmentation from CT scans: A survey. In: Applications of Fuzzy Sets Theory. Berlin: Springer, 2007, pp. 520\u2013528"},{"key":"262_CR26","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1148\/radiol.2341031801","volume":"234","author":"L Hermoye","year":"2005","unstructured":"Hermoye L et al.: Liver Segmentation in Living Liver Transplant Donors: Comparison of Semiautomatic and Manual Methods 1. Radiology 234:171\u2013178, 2005","journal-title":"Radiology"},{"key":"262_CR27","doi-asserted-by":"crossref","unstructured":"Priyadarsini S, Selvathi D: Survey on segmentation of liver from CT images. Proc. 2012 IEEE Int. Conf. Adv. Commun. Control Comput. Technol. ICACCCT, 2012, pp. 234\u2013238","DOI":"10.1109\/ICACCCT.2012.6320777"},{"key":"262_CR28","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.artmed.2008.07.020","volume":"45","author":"P Campadelli","year":"2009","unstructured":"Campadelli P, Casiraghi E, Esposito A: Liver segmentation from computed tomography scans: A survey and a new algorithm. Artif Intell Med 45:185\u2013196, 2009","journal-title":"Artif Intell Med"},{"key":"262_CR29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.4236\/jcc.2014.22001","volume":"2","author":"S Luo","year":"2014","unstructured":"Luo S, Li X, Li J: Review on the Methods of Automatic Liver Segmentation from Abdominal Images. J Comput Commun 2:1\u20137, 2014","journal-title":"J Comput Commun"},{"key":"262_CR30","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1007\/s10462-011-9220-3","volume":"37","author":"AM Mharib","year":"2012","unstructured":"Mharib AM, Ramli AR, Mashohor S, Mahmood RB: Survey on liver CT image segmentation methods. Artif Intell Rev 37:83\u201395, 2012","journal-title":"Artif Intell Rev"},{"key":"262_CR31","doi-asserted-by":"crossref","first-page":"1251","DOI":"10.1109\/TMI.2009.2013851","volume":"28","author":"T Heimann","year":"2009","unstructured":"Heimann T et al.: Comparison and evaluation of methods for liver segmentation from CT datasets. IEEE Trans Med Imaging 28:1251\u20131265, 2009","journal-title":"IEEE Trans Med Imaging"},{"key":"262_CR32","doi-asserted-by":"crossref","unstructured":"Mohammed FA, Viriri S: Liver segmentation: A survey of the state-of-the-art. in 2017 Sudan Conference on Computer Science and Information Technology SCCSIT 1\u20136 IEEE, 2017","DOI":"10.1109\/SCCSIT.2017.8293049"},{"key":"262_CR33","first-page":"275","volume-title":"Error Metrics for Quantitative Evaluation of Medical Image Segmentation","author":"WJ Niessen","year":"2000","unstructured":"Niessen WJ, Bouma CJ, Vincken KL, Viergever MA: Error Metrics for Quantitative Evaluation of Medical Image Segmentation. Dordrecht: Springer, 2000, pp. 275\u2013284"},{"key":"262_CR34","doi-asserted-by":"crossref","first-page":"1207","DOI":"10.1016\/j.neuroimage.2007.04.031","volume":"36","author":"S Bouix","year":"2007","unstructured":"Bouix S et al.: On evaluating brain tissue classifiers without a ground truth. Neuroimage 36:1207\u20131224, 2007","journal-title":"Neuroimage"},{"key":"262_CR35","doi-asserted-by":"crossref","unstructured":"Shimizu A, Nawano S: Preliminary report of competition for liver region extraction algorithms from three-dimensional CT images. Int Congr Ser Elsevier, vol 1268, 2004","DOI":"10.1016\/j.ics.2004.03.312"},{"key":"262_CR36","unstructured":"Rusko L, Bekes G: Fully automatic liver segmentation for contrast-enhanced CT images. Int Conf Med Image Comput Comput Interv Segmentation Clin a Gd challenge, 2007, pp 143\u2013150"},{"issue":"1","key":"262_CR37","first-page":"63","volume":"2","author":"S Kumar","year":"2011","unstructured":"Kumar S, Moni R, Rajeesh J: Automatic segmentation of liver and tumor for CAD of liver. J Adv Inf 2(1):63\u201370, 2011","journal-title":"J Adv Inf"},{"key":"262_CR38","doi-asserted-by":"crossref","first-page":"753","DOI":"10.1097\/01.rli.0000236907.81400.18","volume":"41","author":"B Zhao","year":"2006","unstructured":"Zhao B et al.: Shape-Constraint Region Growing for Delineation of Hepatic Metastases on Contrast-Enhanced Computed Tomograph Scans. Investig Radiol 41:753\u2013762, 2006","journal-title":"Investig Radiol"},{"key":"262_CR39","doi-asserted-by":"crossref","unstructured":"Foruzan AH et al: Multi-mode narrow-band thresholding with application in liver segmentation from low-contrast CT images. IIH-MSP 2009\u20132009 5th Int. Conf. Intell. Inf. Hiding Multimed. Signal Process, 2009, pp 1293\u20131296","DOI":"10.1109\/IIH-MSP.2009.78"},{"key":"262_CR40","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1007\/s11760-011-0223-y","volume":"7","author":"SS Kumar","year":"2013","unstructured":"Kumar SS, Moni RS, Rajeesh J: Automatic liver and lesion segmentation: A primary step in diagnosis of liver diseases. SIViP 7:163\u2013172, 2013","journal-title":"SIViP"},{"key":"262_CR41","doi-asserted-by":"crossref","unstructured":"Wang Q, Song X, Jiang Z: An Improved Image Segmentation Method Using Three-dimensional Region Growing Algorithm. In: Proceedings of the 2013 International Conference on Information Science and Computer Applications ISCA 2013. Atlantis Press, 2013","DOI":"10.2991\/isca-13.2013.26"},{"key":"262_CR42","doi-asserted-by":"crossref","unstructured":"Chen Y, Wang Z, Zhao W, Yang X: Liver Segmentation from CT Images Based on Region Growing Method. 3rd Int. Conf. Bioinforma. Biomed. Eng, 2009, pp 1\u20134","DOI":"10.1109\/ICBBE.2009.5163018"},{"key":"262_CR43","doi-asserted-by":"crossref","unstructured":"Glocker, B. et al. Primal\/Dual Linear Programming and Statistical Atlases for Cartilage Segmentation. in Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2007 Springer Berlin Heidelberg, 536\u2013543, 2007.","DOI":"10.1007\/978-3-540-75759-7_65"},{"key":"262_CR44","unstructured":"Slagmolen P, Elen A, Seghers D, Loeckx D: Atlas based liver segmentation using nonrigid registration with a B-spline transformation model. In Proceedings of MICCAI workshop on 3D segmentation in the clinic: a grand challenge, 2007, pp 197\u2013206"},{"key":"262_CR45","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1016\/0262-8856(94)90060-4","volume":"12","author":"TTF Cootes","year":"1994","unstructured":"Cootes TTF, Hill A, Taylor CJC, Haslam J: Use of active shape models for locating structures in medical images. Image Vis Comput 12:355\u2013365, 1994","journal-title":"Image Vis Comput"},{"key":"262_CR46","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1016\/j.media.2009.05.004","volume":"13","author":"T Heimann","year":"2009","unstructured":"Heimann T, Meinzer H-P: Statistical shape models for 3D medical image segmentation: A review. Med Image Anal 13:543\u2013563, 2009","journal-title":"Med Image Anal"},{"key":"262_CR47","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1109\/TSMCC.2009.2035631","volume":"40","author":"XGX Gao","year":"2010","unstructured":"Gao XGX, Su YSY, Li XLX, Tao DTD: A Review of Active Appearance Models. IEEE Trans Syst Man Cybern Part C Appl Rev 40:145\u2013158, 2010","journal-title":"IEEE Trans Syst Man Cybern Part C Appl Rev"},{"key":"262_CR48","doi-asserted-by":"crossref","unstructured":"Wu W, Wu S, Zhang R, Zhou Z: Fast Graph Cuts Based Liver and Tumor Segmentation on Olumetric CT Images. DEStech Transactions on Engineering and Technology Research, 2016, pp 3\u20137","DOI":"10.12783\/dtetr\/ssme-ist2016\/4028"},{"key":"262_CR49","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cmpb.2017.02.015","volume":"143","author":"M Liao","year":"2017","unstructured":"Liao M et al.: Automatic liver segmentation from abdominal CT volumes using graph cuts and border marching. Comput Methods Prog Biomed 143:1\u201312, 2017","journal-title":"Comput Methods Prog Biomed"},{"key":"262_CR50","unstructured":"Kainm\u00fcller D, Lange T, Lamecker H: Shape constrained automatic segmentation of the liver based on a heuristic intensity model. MICCAI Work. 3D Segmentation Clin. A Gd. Chall, 2007, pp 109\u201316"},{"key":"262_CR51","doi-asserted-by":"crossref","unstructured":"Erdt M, et al: Fast automatic liver segmentation combining learned shape priors with observed shape deviation. Proc - IEEE Symp Comput Med Syst, 2010, pp 249\u2013254","DOI":"10.1109\/CBMS.2010.6042650"},{"key":"262_CR52","first-page":"181","volume-title":"Automatic Liver Segmentation Using Statistical Prior Models and Free-form Deformation","author":"X Li","year":"2014","unstructured":"Li X et al.: Automatic Liver Segmentation Using Statistical Prior Models and Free-form Deformation. Cham: Springer, 2014, pp. 181\u2013188"},{"key":"262_CR53","unstructured":"Boykov YY, Jolly M-P: Interactive Graph Cuts for Optimal Boundary & Region Segmentation of Objects in N-D Images. Proceedings Eighth IEEE International Conference on Computer Vision, ICCV, 2001, pp 105\u201311"},{"key":"262_CR54","doi-asserted-by":"crossref","unstructured":"Platero C, Tobar MC: A multiatlas segmentation using graph cuts with applications to liver segmentation in CT scans. Comput Math Methods Med:182909, 2014","DOI":"10.1155\/2014\/182909"},{"key":"262_CR55","doi-asserted-by":"crossref","first-page":"5315","DOI":"10.1109\/TIP.2015.2481326","volume":"24","author":"G Li","year":"2015","unstructured":"Li G et al.: Automatic Liver Segmentation Based on Shape Constraints and Deformable Graph Cut in CT Images. IEEE Trans Image Process 24:5315\u20135329, 2015","journal-title":"IEEE Trans Image Process"},{"key":"262_CR56","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1007\/BF00133570","volume":"1","author":"M Kass","year":"1988","unstructured":"Kass M, Witkin A, Terzopoulos D: Snakes: Active contour models. Int J Comput Vis 1:321\u2013331, 1988","journal-title":"Int J Comput Vis"},{"key":"262_CR57","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/BF01385685","volume":"66","author":"V Caselles","year":"1993","unstructured":"Caselles V, Catt F, Coll T, Dibos F: A geometric model for active contours in image processing. Numer Math 66:1\u201331, 1993","journal-title":"Numer Math"},{"key":"262_CR58","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/S1361-8415(96)80007-7","volume":"2","author":"T McInerney","year":"1996","unstructured":"McInerney T, Terzopoulos D: Deformable models in medical image analysis: a survey. Med Image Anal 2:91\u2013108, 1996","journal-title":"Med Image Anal"},{"key":"262_CR59","doi-asserted-by":"crossref","first-page":"798","DOI":"10.1587\/transinf.E96.D.798","volume":"E96\u2013D","author":"AH Foruzan","year":"2013","unstructured":"Foruzan AH et al.: Segmentation of liver in low-contrast images using K-means clustering and geodesic active contour algorithms. IEICE Trans Inf Syst E96\u2013D:798\u2013807, 2013","journal-title":"IEICE Trans Inf Syst"},{"key":"262_CR60","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1109\/83.902291","volume":"10","author":"T Chan","year":"2001","unstructured":"Chan T, Chan T, Vese L, Vese L: Active contour without edges. IEEE Trans Image Process 10:266\u2013277, 2001","journal-title":"IEEE Trans Image Process"},{"key":"262_CR61","unstructured":"L\u00e4th\u00e9n G: Segmentation Methods for Medical Image Analysis : Blood vessels, multi-scale filtering and level set methods, 2010"},{"key":"262_CR62","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1155\/2014\/198015","volume":"2014","author":"M Goryawala","year":"2014","unstructured":"Goryawala M, Gulec S, Bhatt R, McGoron AJ, Adjouadi M: A Low-Interaction Automatic 3D Liver Segmentation Method Using Computed Tomography for Selective Internal Radiation Therapy. Biomed Res Int 2014:12, 2014","journal-title":"Biomed Res Int"},{"key":"262_CR63","first-page":"1","volume":"6","author":"NM Altarawneh","year":"2015","unstructured":"Altarawneh NM, Luo S, Regan B, Sun C: A Modified Distance Regularized Level Set Model for Liver Segmentation from CT Images. An Int J 6:1\u20131, 2015","journal-title":"An Int J"},{"key":"262_CR64","doi-asserted-by":"crossref","first-page":"9661","DOI":"10.1016\/j.eswa.2012.02.095","volume":"39","author":"BN Li","year":"2012","unstructured":"Li BN, Chui CK, Chang S, Ong SH: A new unified level set method for semi-automatic liver tumor segmentation on contrast-enhanced CT images. Expert Syst Appl 39:9661\u20139668, 2012","journal-title":"Expert Syst Appl"},{"key":"262_CR65","doi-asserted-by":"crossref","first-page":"1965","DOI":"10.1109\/TMI.2012.2211887","volume":"31","author":"MG Linguraru","year":"2012","unstructured":"Linguraru MG et al.: Tumor burden analysis on computed tomography by automated liver and tumor segmentation. IEEE Trans Med Imaging 31:1965\u20131976, 2012","journal-title":"IEEE Trans Med Imaging"},{"key":"262_CR66","unstructured":"Anter AM, Azar AT, Hassanien AE, El-Bendary N, Elsoud MA: Automatic computer aided segmentation for liver and hepatic lesions using hybrid segmentations techniques. 2013 Fed Conf Comput Sci Inf Syst FedCSIS, 2013, pp 193\u2013198"},{"key":"262_CR67","first-page":"1","volume":"41","author":"Y Qi","year":"2008","unstructured":"Qi Y et al.: Semi-automatic segmentation of liver tumors from CT scans using Bayesian rule-based 3D region growing. MICCAI Work. 41:1\u201310, 2008","journal-title":"MICCAI Work."},{"key":"262_CR68","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1186\/1475-925X-10-30","volume":"10","author":"DA Oliveira","year":"2011","unstructured":"Oliveira DA, Feitosa RQ, Correia MM: Segmentation of liver, its vessels and lesions from CT images for surgical planning. Biomed Eng Online 10:30, 2011","journal-title":"Biomed Eng Online"},{"key":"262_CR69","doi-asserted-by":"crossref","first-page":"1738","DOI":"10.1007\/s00330-010-1712-z","volume":"20","author":"JY Zhou","year":"2010","unstructured":"Zhou JY et al.: Liver tumour segmentation using contrast-enhanced multi-detector CT data: Performance benchmarking of three semiautomated methods. Eur Radiol 20:1738\u20131748, 2010","journal-title":"Eur Radiol"},{"key":"262_CR70","first-page":"195","volume":"41","author":"JH Moltz","year":"2008","unstructured":"Moltz JH, Bornemann L, Dicken V, Peitgen H-O: Segmentation of liver metastases in CT scans by adaptive thresholding and morphological processing. MICCAI Work 41:195, 2008","journal-title":"MICCAI Work"},{"key":"262_CR71","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.media.2011.06.006","volume":"16","author":"Y H\u00e4me","year":"2012","unstructured":"H\u00e4me Y, Pollari M: Semi-automatic liver tumor segmentation with hidden Markov measure field model and non-parametric distribution estimation. Med Image Anal 16:140\u2013149, 2012","journal-title":"Med Image Anal"},{"key":"262_CR72","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1007\/s11548-010-0497-5","volume":"6","author":"M Freiman","year":"2011","unstructured":"Freiman M, Cooper O, Lischinski D, Joskowicz L: Liver tumors segmentation from CTA images using voxels classification and affinity constraint propagation. Int J Comput Assist Radiol Surg 6:247\u2013255, 2011","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"262_CR73","unstructured":"Huang W, et al: Liver Tumor Detection and Segmentation using Kernel-based Extreme Learning Machine. In: Engineering in medicine and biology society (EMBC), 2013 35th annual international conference of the IEEE, 138632, 2013, pp 3662\u20133665"},{"key":"262_CR74","first-page":"1","volume":"2017","author":"W Wu","year":"2017","unstructured":"Wu W, Wu S, Zhou Z, Zhang R, Zhang Y: 3D Liver Tumor Segmentation in CT Images Using Improved Fuzzy C -Means and Graph Cuts. Biomed Res Int 2017:1\u201311, 2017","journal-title":"Biomed Res Int"},{"key":"262_CR75","first-page":"74","volume-title":"Metastatic Liver Tumor Segmentation Using Texture-Based Omni-Directional Deformable Surface Models","author":"E Vorontsov","year":"2014","unstructured":"Vorontsov E, Abi-Jaoudeh N, Kadoury S: Metastatic Liver Tumor Segmentation Using Texture-Based Omni-Directional Deformable Surface Models. Cham: Springer, 2014, pp. 74\u201383"},{"key":"262_CR76","first-page":"23","volume":"11","author":"N Otsu","year":"1975","unstructured":"Otsu N: A threshold selection method from gray-level histograms. Automatica 11:23\u201327, 1975","journal-title":"Automatica"},{"issue":"1","key":"262_CR77","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1007\/s00357-001-0004-3","volume":"18","author":"A Chaturvedi","year":"2001","unstructured":"Chaturvedi A, Green P, Caroll J: K-modes clustering. J Classif 18(1):35\u201355, 2001","journal-title":"J Classif"},{"key":"262_CR78","first-page":"899","volume":"205","author":"Z Huang","year":"2008","unstructured":"Huang Z, Chau K: A new image thresholding method based on Gaussian mixture model q. Appl Math Comput 205:899\u2013907, 2008","journal-title":"Appl Math Comput"},{"key":"262_CR79","unstructured":"Rajagopal R, Subbaiah P: A survey on liver tumor detection and segmentation methods. 10(6): 2681\u20132685, 2015 ."},{"issue":"1","key":"262_CR80","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1007\/s11760-011-0223-y","volume":"7","author":"SSKRSSMJ Rajeesh","year":"2013","unstructured":"Rajeesh SSKRSSMJ: Automatic liver and lesion segmentation : a primary step in diagnosis of liver diseases. SIViP 7(1):163\u2013172, 2013","journal-title":"SIViP"},{"key":"262_CR81","doi-asserted-by":"crossref","unstructured":"Pamulapati V, Wood BJ, Linguraru MG: Intra-hepatic vessel segmentation and classification in multi-phase CT using optimized graph cuts. in Biomedical Imaging: From Nano to Macro, 2011 IEEE International Symposium, 2011, pp 1982\u20131985","DOI":"10.1109\/ISBI.2011.5872799"},{"key":"262_CR82","doi-asserted-by":"crossref","first-page":"2144","DOI":"10.1109\/TBME.2010.2093523","volume":"58","author":"Y Chi","year":"2011","unstructured":"Chi Y et al.: Segmentation of liver vasculature from contrast enhanced CT images using context-based voting. IEEE Trans Biomed Eng 58:2144\u20132153, 2011","journal-title":"IEEE Trans Biomed Eng"},{"key":"262_CR83","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1016\/j.acra.2010.11.015","volume":"18","author":"F Conversano","year":"2011","unstructured":"Conversano F et al.: Hepatic Vessel Segmentation for 3D Planning of Liver Surgery. Experimental Evaluation of a New Fully Automatic Algorithm. Acad Radiol 18:461\u2013470, 2011","journal-title":"Acad Radiol"},{"key":"262_CR84","doi-asserted-by":"crossref","first-page":"498","DOI":"10.4236\/jbise.2013.64063","volume":"06","author":"D Kim","year":"2013","unstructured":"Kim D: Hepatic vessel segmentation on contrast enhanced CT image sequence for liver transplantation planning. J Biomed Sci Eng 06:498\u2013503, 2013","journal-title":"J Biomed Sci Eng"},{"key":"262_CR85","first-page":"1","volume":"2013","author":"J Jin","year":"2013","unstructured":"Jin J, Yang L, Zhang X, Ding M: Vascular tree segmentation in medical images using Hessian-based multiscale filtering and level set method. Comput Math Methods Med 2013:1\u201310, 2013","journal-title":"Comput Math Methods Med"},{"key":"262_CR86","first-page":"8314M","volume":"8314","author":"A Yureidini","year":"2012","unstructured":"Yureidini A, Kerrien E, Loria ING, Nord-europe IL: Robust RANSAC-based blood vessel segmentation. Proc SPIE 8314:8314M, 2012","journal-title":"Proc SPIE"},{"key":"262_CR87","doi-asserted-by":"crossref","first-page":"1435","DOI":"10.1117\/12.535514","volume":"5370","author":"R Beichel","year":"2004","unstructured":"Beichel R et al.: Liver segment approximation in CT data for surgical resection planning. Proc SPIE 5370:1435\u20131446, 2004","journal-title":"Proc SPIE"},{"key":"262_CR88","doi-asserted-by":"crossref","first-page":"267","DOI":"10.2478\/raon-2014-0022","volume":"48","author":"M Marcan","year":"2014","unstructured":"Marcan M et al.: Segmentation of hepatic vessels from MRI images for planning of electroporation-based treatments in the liver. Radiol Oncol 48:267\u2013268, 2014","journal-title":"Radiol Oncol"},{"key":"262_CR89","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1109\/TBME.2009.2032161","volume":"57","author":"S Esneault","year":"2010","unstructured":"Esneault S, Lafon C, Dillenseger J-L: Liver Vessels Segmentation Using a Hybrid Geometrical Moments\/Graph Cuts Method. IEEE Trans Biomed Eng 57:276\u2013283, 2010","journal-title":"IEEE Trans Biomed Eng"},{"key":"262_CR90","doi-asserted-by":"crossref","first-page":"819","DOI":"10.1016\/j.media.2009.07.011","volume":"13","author":"D Lesage","year":"2009","unstructured":"Lesage D, Angelini ED, Bloch I, Funka-Lea G: A review of 3D vessel lumen segmentation techniques: Models, features and extraction schemes. Med Image Anal 13:819\u2013845, 2009","journal-title":"Med Image Anal"},{"key":"262_CR91","doi-asserted-by":"crossref","unstructured":"Rodrigues FM, Silva JS, Rodrigues TM: An algorithm for the surgical planning of hepatic resections. 2012 IEEE 2nd Port. Meet Bioeng ENBENG, 2012, pp 1\u20136","DOI":"10.1109\/ENBENG.2012.6331384"},{"key":"262_CR92","unstructured":"Mohan V, Sundaramoorthi G, Stillman A, Tannenbaum A: Vessel Segmentation with Automatic Centerline Extraction Using Tubular Tree Segmentation. 8, 2009"},{"key":"262_CR93","unstructured":"Shen Y, Wang B, Ju Y, Xie J: Interaction techniques for the exploration of hepatic vessel structure. Eng Med 2902\u20132905,2006"},{"key":"262_CR94","doi-asserted-by":"crossref","first-page":"131","DOI":"10.3109\/10929080109145999","volume":"6","author":"L Soler","year":"2001","unstructured":"Soler L et al.: Fully automatic anatomical, pathological, and functional segmentation from CT scans for hepatic surgery. Comput Aided Surg 6:131\u2013142, 2001","journal-title":"Comput Aided Surg"},{"key":"262_CR95","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1007\/s11548-013-0956-x","volume":"9","author":"E Smistad","year":"2014","unstructured":"Smistad E, Elster AC, Lindseth F: GPU accelerated segmentation and centerline extraction of tubular structures from medical images. Int J Comput Assist Radiol Surg 9:561\u2013575, 2014","journal-title":"Int J Comput Assist Radiol Surg"},{"issue":"2","key":"262_CR96","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1145\/1031120.1031121","volume":"36","author":"C Kirbas","year":"2003","unstructured":"Kirbas C, Quek F: A Review of Vessel Extraction Techniques and Algorithms. ACM Comput Surv 36(2):81\u2013121, 2003","journal-title":"ACM Comput Surv"},{"key":"262_CR97","first-page":"1","volume":"2014","author":"Y Tian","year":"2014","unstructured":"Tian Y et al.: A vessel active contour model for vascular segmentation. Biomed Res Int 2014:1\u201315, 2014","journal-title":"Biomed Res Int"},{"key":"262_CR98","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1186\/1475-925X-13-169","volume":"13","author":"Q Hong","year":"2014","unstructured":"Hong Q et al.: 3D vasculature segmentation using localized hybrid level-set method. Biomed Eng Online 13:169, 2014","journal-title":"Biomed Eng Online"},{"key":"262_CR99","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1007\/BFb0056195","volume":"1496","author":"AF Frangi","year":"1998","unstructured":"Frangi AF, Niessen WJ, Vincken KL, Viergever MA: Multiscale vessel enhancement filtering. Medial Image Comput. Comput. Invervention - MICCAI\u201998. Lect Notes Comput Sci 1496:130\u2013137, 1998","journal-title":"Lect Notes Comput Sci"},{"key":"262_CR100","doi-asserted-by":"crossref","first-page":"815","DOI":"10.1016\/j.media.2006.06.003","volume":"10","author":"R Manniesing","year":"2006","unstructured":"Manniesing R, Viergever MA, Niessen WJ: Vessel enhancing diffusion. A scale space representation of vessel structures. Med Image Anal 10:815\u2013825, 2006","journal-title":"Med Image Anal"},{"key":"262_CR101","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/S1361-8415(98)80009-1","volume":"2","author":"Y Sato","year":"1998","unstructured":"Sato Y et al.: Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images. Med Image Anal 2:143\u2013168, 1998","journal-title":"Med Image Anal"},{"key":"262_CR102","doi-asserted-by":"crossref","unstructured":"Erdt M, Raspe M, Suehling M: Automatic Hepatic Vessel Segmentation Using Graphics Hardware. In: Medical Imaging and Augmented Reality, 2008, pp 403\u2013412","DOI":"10.1007\/978-3-540-79982-5_44"},{"key":"262_CR103","doi-asserted-by":"crossref","first-page":"3905","DOI":"10.1088\/0031-9155\/60\/10\/3905","volume":"60","author":"HM Luu","year":"2015","unstructured":"Luu HM, Klink C, Moelker A, Niessen W, van Walsum T: Quantitative evaluation of noise reduction and vesselness filters for liver vessel segmentation on abdominal CTA images. Phys Med Biol 60:3905\u20133926, 2015","journal-title":"Phys Med Biol"},{"key":"262_CR104","doi-asserted-by":"crossref","first-page":"1585","DOI":"10.1109\/TMI.2009.2022368","volume":"28","author":"AM Mendrik","year":"2009","unstructured":"Mendrik AM, Vonken EJ, Rutten A, Viergever MA, Van Ginneken B: Noise reduction in computed tomography scans using 3-D anisotropic hybrid diffusion with continuous switch. IEEE Trans Med Imaging 28:1585\u20131594, 2009","journal-title":"IEEE Trans Med Imaging"},{"key":"262_CR105","doi-asserted-by":"crossref","first-page":"761","DOI":"10.1007\/s00276-010-0626-4","volume":"32","author":"JHD Fasel","year":"2010","unstructured":"Fasel JHD, Majno PE, Peitgen H-O: Liver segments: an anatomical rationale for explaining inconsistencies with Couinaud\u2019s eight-segment concept. Surg Radiol Anat 32:761\u2013765, 2010","journal-title":"Surg Radiol Anat"},{"key":"262_CR106","unstructured":"Castaing D, Adam R, Azoulay D: Chirurgie du foie et de l\u2019hypertension portale. Masson, 2006"},{"key":"262_CR107","doi-asserted-by":"crossref","unstructured":"Drechsler K, Erdt M, Laura CO, Wesarg S: Multiphase risk assessment of atypical liver resections. in Computer-Based Medical Systems CBMS , 2012 25th International Symposium on 1\u20134 IEEE, 2012","DOI":"10.1109\/CBMS.2012.6266316"},{"key":"262_CR108","unstructured":"Yoon JH, et al: Feasibility of three-dimensional virtual surgical planning in living liver donors. Abdom Imaging, 1\u201311,2014"},{"key":"262_CR109","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1109\/MCG.2006.131","volume":"26","author":"B Reitinger","year":"2006","unstructured":"Reitinger B, Bornik A, Beichel R, Schmalstieg D: Liver Surgery Planning Using Virtual Reality. IEEE Comput Graph Appl 26:36\u201347, 2006","journal-title":"IEEE Comput Graph Appl"},{"key":"262_CR110","doi-asserted-by":"crossref","unstructured":"Debarba HG, Zanchet DJ, Fracaro D, MacIel A, Kalil AN: Efficient liver surgery planning in 3D based on functional segment classification and volumetric information. 2010 Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBC\u201910, 2010, pp 4797\u20134800","DOI":"10.1109\/IEMBS.2010.5628026"},{"key":"262_CR111","doi-asserted-by":"crossref","first-page":"709","DOI":"10.1016\/j.ejmp.2016.04.003","volume":"32","author":"Y Zhan","year":"2016","unstructured":"Zhan Y et al.: Liver vessel segmentation based on extreme learning machine. Phys Medica 32:709\u2013716, 2016","journal-title":"Phys Medica"},{"key":"262_CR112","doi-asserted-by":"crossref","first-page":"725911","DOI":"10.1117\/12.812407","volume":"725911","author":"JN Kaftan","year":"2009","unstructured":"Kaftan JN, Tek H, Aach T: A two-stage approach for fully automatic segmentation of venous vascular structures in liver CT images. Proc SPIE 725911:725911\u2013725912, 2009","journal-title":"Proc SPIE"},{"key":"262_CR113","first-page":"7391","volume":"11","author":"HE Blum","year":"2005","unstructured":"Blum HE: Hepatocellular carcinoma: therapy and prevention. World J Gastroenterol 11:7391\u20137400, 2005","journal-title":"World J Gastroenterol"},{"key":"262_CR114","doi-asserted-by":"crossref","DOI":"10.1002\/9781444317053","volume-title":"Malignant liver tumors : current and emerging therapies","author":"P-A Clavien","year":"2010","unstructured":"Clavien P-A, Breitenstein S, Belghiti J: Malignant liver tumors : current and emerging therapies. Hoboken: Wiley-Blackwell Pub, 2010"},{"key":"262_CR115","doi-asserted-by":"crossref","first-page":"1245","DOI":"10.1016\/S0140-6736(11)61347-0","volume":"379","author":"A Forner","year":"2012","unstructured":"Forner A, Llovet JM, Bruix J: Hepatocellular carcinoma. Lancet 379:1245\u20131255, 2012","journal-title":"Lancet"},{"key":"262_CR116","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1148\/radiol.10091897","volume":"256","author":"KW Kim","year":"2010","unstructured":"Kim KW et al.: Right Lobe Estimated Blood-free Weight for Living Donor Liver Transplantation: Accuracy of Automated Blood-free CT Volumetry\u2014Preliminary Results. Radiology 256:433\u2013440, 2010","journal-title":"Radiology"},{"key":"262_CR117","doi-asserted-by":"crossref","first-page":"e110201","DOI":"10.1371\/journal.pone.0110201","volume":"9","author":"T Mokry","year":"2014","unstructured":"Mokry T et al.: Accuracy of Estimation of Graft Size for Living-Related Liver Transplantation: First Results of a Semi-Automated Interactive Software for CT-Volumetry. PLoS One 9:e110201, 2014","journal-title":"PLoS One"},{"key":"262_CR118","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.cmpb.2018.01.024","volume":"158","author":"X Yang","year":"2018","unstructured":"Yang X et al.: Dr Liver: A preoperative planning system of liver graft volumetry for living donor liver transplantation. Comput Methods Prog Biomed 158:11\u201319, 2018","journal-title":"Comput Methods Prog Biomed"}],"container-title":["Journal of Digital Imaging"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-019-00262-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10278-019-00262-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-019-00262-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,8,17]],"date-time":"2020-08-17T23:50:26Z","timestamp":1597708226000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10278-019-00262-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,8,19]]},"references-count":118,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2020,4]]}},"alternative-id":["262"],"URL":"https:\/\/doi.org\/10.1007\/s10278-019-00262-8","relation":{},"ISSN":["0897-1889","1618-727X"],"issn-type":[{"value":"0897-1889","type":"print"},{"value":"1618-727X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,8,19]]},"assertion":[{"value":"19 August 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with Ethical Standards"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}},{"value":"All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and\/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. For this type of study, formal consent is not required.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}},{"value":"Informed consent was obtained from all individual participants included in the study.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed Consent"}}]}}