{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T04:25:55Z","timestamp":1782188755018,"version":"3.54.5"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2020,1,16]],"date-time":"2020-01-16T00:00:00Z","timestamp":1579132800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,16]],"date-time":"2020-01-16T00:00:00Z","timestamp":1579132800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 51608070"],"award-info":[{"award-number":["No. 51608070"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 61309013"],"award-info":[{"award-number":["No. 61309013"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002865","name":"Chongqing Science and Technology Commission","doi-asserted-by":"publisher","award":["o. CSTC2014JCYJA40042"],"award-info":[{"award-number":["o. CSTC2014JCYJA40042"]}],"id":[{"id":"10.13039\/501100002865","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002865","name":"Chongqing Science and Technology Commission","doi-asserted-by":"publisher","award":["No. CSTC2016JCYJA0022"],"award-info":[{"award-number":["No. CSTC2016JCYJA0022"]}],"id":[{"id":"10.13039\/501100002865","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2020,6]]},"DOI":"10.1007\/s00521-019-04700-0","type":"journal-article","created":{"date-parts":[[2020,1,16]],"date-time":"2020-01-16T15:03:18Z","timestamp":1579186998000},"page":"6769-6778","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":63,"title":["A two-stage approach for automatic liver segmentation with Faster R-CNN and DeepLab"],"prefix":"10.1007","volume":"32","author":[{"given":"Wei","family":"Tang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8001-6461","authenticated-orcid":false,"given":"Dongsheng","family":"Zou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Su","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Shi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingpei","family":"Dan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guowu","family":"Song","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,1,16]]},"reference":[{"key":"4700_CR1","unstructured":"Alshaikhli SDS, Yang MY, Rosenhahn B (2015) Automatic 3D liver segmentation using sparse representation of global and local image information via level set formulation. arXiv:1508.01521"},{"issue":"2","key":"4700_CR2","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1007\/s11263-006-7934-5","volume":"70","author":"Y Boykov","year":"2006","unstructured":"Boykov Y, Funka-Lea G (2006) Graph cuts and efficient N-D image segmentation. Int J Comput Vis 70(2):109\u2013131","journal-title":"Int J Comput Vis"},{"issue":"3","key":"4700_CR3","first-page":"59","volume":"4","author":"H Burkhardt","year":"2010","unstructured":"Burkhardt H (2010) Integration of morphology and graph-based techniques for fully automatic liver segmentation. Majlesi J Electr Eng 4(3):59\u201366","journal-title":"Majlesi J Electr Eng"},{"issue":"2","key":"4700_CR4","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 (2009) Liver segmentation from CT scans: a survey. Artif Intell Med 45(2):185\u2013196","journal-title":"Artif Intell Med"},{"key":"4700_CR5","first-page":"357","volume":"4","author":"LC Chen","year":"2014","unstructured":"Chen LC, Papandreou G, Kokkinos I et al (2014) Semantic image segmentation with deep convolutional nets and fully connected CRFs. Comput Sci 4:357\u2013361","journal-title":"Comput Sci"},{"issue":"4","key":"4700_CR6","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","volume":"40","author":"LC Chen","year":"2017","unstructured":"Chen LC, Papandreou G, Kokkinos I et al (2017) DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans Pattern Anal Mach Intell 40(4):834\u2013848","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"6","key":"4700_CR7","doi-asserted-by":"crossref","first-page":"705","DOI":"10.1016\/j.cviu.2013.01.011","volume":"117","author":"F Chung","year":"2013","unstructured":"Chung F, Delingette H (2013) Regional appearance modeling based on the clustering of intensity profiles. Comput Vis Image Underst 117(6):705\u2013717","journal-title":"Comput Vis Image Underst"},{"key":"4700_CR8","unstructured":"Dawant BM, Li R, Lennon B et al (2007) Semi-automatic segmentation of the liver and its evaluation on the MICCAI 2007 grand challenge data set. In: Proceedings of MICCAI workshop 3D segmentation clinic: a grand challenge, pp 215\u2013221"},{"key":"4700_CR9","unstructured":"Dong C, Chen YW, Tateyama T et al (2016) A knowledge-based interactive liver segmentation using random walks. In: International conference on fuzzy systems and knowledge discovery. IEEE, pp 1731\u20131736"},{"key":"4700_CR10","doi-asserted-by":"crossref","unstructured":"Dou Q, Chen H, Jin Y et al (2016) 3D Deeply supervised network for automatic liver segmentation from CT volumes, pp 149\u2013157","DOI":"10.1007\/978-3-319-46723-8_18"},{"key":"4700_CR11","doi-asserted-by":"crossref","unstructured":"Erdt M, Steger S, Kirschner M et al (2010) Fast automatic liver segmentation combining learned shape priors with observed shape deviation. In: IEEE, international symposium on computer-based medical systems. IEEE, pp 249\u2013254","DOI":"10.1109\/CBMS.2010.6042650"},{"key":"4700_CR12","doi-asserted-by":"crossref","unstructured":"Gambino O, Vitabile S, Re GL et al (2010) Automatic volumetric liver segmentation using texture based region growing. In: International conference on complex, intelligent and software intensive systems. IEEE Computer Society, pp 146\u2013152","DOI":"10.1109\/CISIS.2010.118"},{"key":"4700_CR13","unstructured":"Ginneken BV, Heimann T, Styner M (2007) 3D Segmentation in the clinic: a grand challenge. In: Proceedings of 3D segmentation in the clinic: a grand challenge. Springer, Brisbane, pp 7\u201315"},{"key":"4700_CR14","doi-asserted-by":"crossref","unstructured":"Girshick R (2015) Fast R-CNN. In: IEEE international conference on computer vision. IEEE, pp 1440\u20131448","DOI":"10.1109\/ICCV.2015.169"},{"key":"4700_CR15","doi-asserted-by":"crossref","unstructured":"Girshick R, Donahue J, Darrell T et al (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: IEEE international conference on computer vision and pattern recognition, pp 580\u2013587","DOI":"10.1109\/CVPR.2014.81"},{"issue":"5","key":"4700_CR16","doi-asserted-by":"crossref","first-page":"2421","DOI":"10.1118\/1.4946817","volume":"43","author":"B He","year":"2016","unstructured":"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(5):2421","journal-title":"Med Phys"},{"key":"4700_CR17","unstructured":"Heimann T, Meinzer HP, Wolf I (2010) A statistical deformable model for the segmentation of liver CT volumes. In: MICCAI workshop on 3D Segmentation in the Clinic"},{"issue":"8","key":"4700_CR18","doi-asserted-by":"crossref","first-page":"1251","DOI":"10.1109\/TMI.2009.2013851","volume":"28","author":"T Heimann","year":"2009","unstructured":"Heimann T, Van GB, Styner MA et al (2009) Comparison and evaluation of methods for liver segmentation from CT datasets. IEEE Trans Med Imaging 28(8):1251\u20131265","journal-title":"IEEE Trans Med Imaging"},{"issue":"1","key":"4700_CR19","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1007\/s11548-015-1254-6","volume":"11","author":"J Jansen","year":"2015","unstructured":"Jansen J, Schreurs R, Dubois L et al (2015) Orbital volume analysis: validation of a semi-automatic software segmentation method. Int J Comput Assist Radiol Surg 11(1):11\u201318","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"4700_CR20","unstructured":"Kainm\u00fcller D, Lange T, Lamecker H (2007) Shape constrained automatic segmentation of the liver based on a heuristic intensity model. In: MICCAI workshop on 3D Segmentation in the Clinic, pp 109\u2013116"},{"key":"4700_CR21","unstructured":"Saddi KA, Rousson M et al (2007) Global-to-local shape matching for liver segmentation in CT imaging"},{"key":"4700_CR22","unstructured":"Kirschner M (2013) The probabilistic active shape model: from model construction to flexible medical image segmentation. PhD dissertation"},{"key":"4700_CR23","unstructured":"Kr\u00e4henb\u00fchl P, Koltun V (2011) Efficient inference in fully connected CRFs with Gaussian edge potentials. In: Proceedings of advances in neural information processing systems, vol 24, pp 109\u2013117"},{"issue":"11","key":"4700_CR24","doi-asserted-by":"crossref","first-page":"1383","DOI":"10.1016\/j.ejmp.2016.10.002","volume":"32","author":"M Liao","year":"2016","unstructured":"Liao M, Zhao YQ, Wang W et al (2016) Efficient liver segmentation in CT images based on graph cuts and bottleneck detection. Phys Med 32(11):1383","journal-title":"Phys Med"},{"issue":"12","key":"4700_CR25","doi-asserted-by":"crossref","first-page":"5315","DOI":"10.1109\/TIP.2015.2481326","volume":"24","author":"G Li","year":"2015","unstructured":"Li G, Chen X, Shi F et al (2015) Automatic liver segmentation based on shape constraints and deformable graph cut in CT Images. IEEE Trans Image Process 24(12):5315","journal-title":"IEEE Trans Image Process"},{"key":"4700_CR26","first-page":"198","volume-title":"Lecture Notes in Computer Science","author":"Marius George Linguraru","year":"2012","unstructured":"Linguraru MG, Richbourg WJ, Watt JM et al (2011) Liver and tumor segmentation and analysis from CT of diseased patients via a generic affine invariant shape parameterization and graph cuts. In: International conference on abdominal imaging: computational and clinical applications. Springer, Berlin, pp 198\u2013206"},{"issue":"2","key":"4700_CR27","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1007\/s11548-016-1467-3","volume":"12","author":"F Lu","year":"2017","unstructured":"Lu F, Wu F, Hu P et al (2017) Automatic 3D liver location and segmentation via convolutional neural network and graph cut. Int J Comput Assist Radiol Surg 12(2):171\u2013182","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"4700_CR28","doi-asserted-by":"crossref","unstructured":"Lu J, Shi L, Deng M et al (2011) An interactive approach to liver segmentation in CT based on deformable model integrated with attractor force. In: International conference on machine learning and cybernetics. IEEE, pp 1660\u20131665","DOI":"10.1109\/ICMLC.2011.6016986"},{"key":"4700_CR29","doi-asserted-by":"crossref","first-page":"133","DOI":"10.3389\/fendo.2017.00133","volume":"8","author":"W Njg","year":"2017","unstructured":"Njg W (2017) Alternative RNA splicing in the pathogenesis of liver disease. Front Endocrinol 8:133","journal-title":"Front Endocrinol"},{"issue":"6","key":"4700_CR30","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2015","unstructured":"Ren S, He K, Girshick R et al (2015) Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39(6):1137","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"4700_CR31","doi-asserted-by":"crossref","unstructured":"Schmidt G, Athelogou MA, Sch\u00f6nmeyer R et al (2007) Cognition network technology for a fully automated 3-D segmentation of liver. In: MICCAI workshop on 3D Segmentation in the clinic: a grand challenge","DOI":"10.1055\/s-2008-1073929"},{"key":"4700_CR32","doi-asserted-by":"crossref","unstructured":"Wang N, Huang L L, Zhang B (2010) A fast hybrid method for interactive liver segmentation. In: Chinese conference on pattern recognition, pp 1\u20135","DOI":"10.1109\/CCPR.2010.5659281"},{"key":"4700_CR33","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1007\/978-3-642-04271-3_4","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2009","author":"Andreas Wimmer","year":"2009","unstructured":"Wimmer A, Soza G, Hornegger J (2009) A generic probabilistic active shape model for organ segmentation. In: Medical image computing and computer-assisted intervention\u2013MICCAI 2009. Springer, Berlin, pp 26\u201333"},{"issue":"11","key":"4700_CR34","doi-asserted-by":"crossref","first-page":"6283","DOI":"10.1118\/1.4932365","volume":"42","author":"J Yan","year":"2015","unstructured":"Yan J, Schwartz LH, Zhao B (2015) Semiautomatic segmentation of liver metastases on volumetric CT images. Med Phys 42(11):6283\u20136293","journal-title":"Med Phys"},{"key":"4700_CR35","doi-asserted-by":"crossref","unstructured":"Yang D, Xu D, Zhou S K et al (2017) Automatic liver segmentation using an adversarial image-to-image network","DOI":"10.1007\/978-3-319-66179-7_58"},{"issue":"1","key":"4700_CR36","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.cmpb.2013.08.019","volume":"113","author":"X Yang","year":"2014","unstructured":"Yang X, Yu HC, Choi Y et al (2014) A hybrid semi-automatic method for liver segmentation based on level-set methods using multiple seed points. Comput Methods Programs Biomed 113(1):69\u201379","journal-title":"Comput Methods Programs Biomed"},{"issue":"4","key":"4700_CR37","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1109\/TPAMI.2016.2572683","volume":"39","author":"E Shelhamer","year":"2014","unstructured":"Shelhamer E, Long J, Darrell T (2014) Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell 39(4):640\u2013651","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"4700_CR38","doi-asserted-by":"crossref","unstructured":"Shrivastava A, Gupta A, Girshick R (2016) Training region-based object detectors with online hard example mining. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 761\u2013769","DOI":"10.1109\/CVPR.2016.89"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-019-04700-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00521-019-04700-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-019-04700-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,1,15]],"date-time":"2021-01-15T01:38:42Z","timestamp":1610674722000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00521-019-04700-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,16]]},"references-count":38,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2020,6]]}},"alternative-id":["4700"],"URL":"https:\/\/doi.org\/10.1007\/s00521-019-04700-0","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,1,16]]},"assertion":[{"value":"4 January 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 December 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 January 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}