{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T05:20:32Z","timestamp":1769836832512,"version":"3.49.0"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2019,11,4]],"date-time":"2019-11-04T00:00:00Z","timestamp":1572825600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2019,11,4]],"date-time":"2019-11-04T00:00:00Z","timestamp":1572825600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J CARS"],"published-print":{"date-parts":[[2020,2]]},"DOI":"10.1007\/s11548-019-02085-y","type":"journal-article","created":{"date-parts":[[2019,11,4]],"date-time":"2019-11-04T17:03:17Z","timestamp":1572886997000},"page":"249-257","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Incorporating prior shape knowledge via data-driven loss model to improve 3D liver segmentation in deep CNNs"],"prefix":"10.1007","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9923-6117","authenticated-orcid":false,"given":"Saeed","family":"Mohagheghi","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0177-3227","authenticated-orcid":false,"given":"Amir Hossein","family":"Foruzan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,11,4]]},"reference":[{"key":"2085_CR1","doi-asserted-by":"publisher","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 (2009) Liver segmentation from computed tomography scans: a survey and a new algorithm. Artif Intell Med 45:185\u2013196","journal-title":"Artif Intell Med"},{"key":"2085_CR2","doi-asserted-by":"publisher","first-page":"1251","DOI":"10.1109\/TMI.2009.2013851","volume":"28","author":"T Heimann","year":"2009","unstructured":"Heimann T, Van Ginneken B, Styner MA, Arzhaeva Y, Aurich V, Bauer C, Beck A, Becker C, Beichel R, Bekes G (2009) Comparison and evaluation of methods for liver segmentation from CT datasets. IEEE Trans Med Imaging 28:1251\u20131265","journal-title":"IEEE Trans Med Imaging"},{"key":"2085_CR3","unstructured":"Chen H, Dou Q, Yu L, Chen H, Jin Y, Yang X, Qin J, Heng P (2016) 3D deeply supervised network for automatic liver segmentation from CT volumes 3D deeply supervised network for automated segmentation of volumetric medical images. In: International conference on medical image computing and computer-assisted intervention. Springer, Berlin, pp 149\u2013157"},{"key":"2085_CR4","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1007\/s11548-016-1467-3","volume":"12","author":"F Lu","year":"2017","unstructured":"Lu F, Wu F, Hu P, Peng Z, Kong D (2017) Automatic 3D liver location and segmentation via convolutional neural network and graph cut. Int J Comput Assist Radiol Surg 12:171\u2013182","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"2085_CR5","doi-asserted-by":"publisher","first-page":"8676","DOI":"10.1088\/1361-6560\/61\/24\/8676","volume":"61","author":"P Hu","year":"2016","unstructured":"Hu P, Wu F, Peng J, Liang P, Kong D (2016) Automatic 3D liver segmentation based on deep learning and globally optimized surface evolution. Phys Med Biol 61:8676","journal-title":"Phys Med Biol"},{"key":"2085_CR6","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1186\/s12880-015-0068-x","volume":"15","author":"AA Taha","year":"2015","unstructured":"Taha AA, Hanbury A (2015) Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Med Imaging 15:29","journal-title":"BMC Med Imaging"},{"key":"2085_CR7","unstructured":"Masci J, Meier U, Ciresan D, Schmidhuber J (2011) Stacked convolutional AEs for hierarchical feature extraction. In: Icann. Springer, Berlin, pp 52\u201359"},{"key":"2085_CR8","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.media.2016.10.004","volume":"36","author":"K Kamnitsas","year":"2017","unstructured":"Kamnitsas K, Ledig C, Newcombe VFJ, Simpson JP, Kane AD, Menon DK, Rueckert D, Glocker B (2017) Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med Image Anal 36:61\u201378","journal-title":"Med Image Anal"},{"key":"2085_CR9","doi-asserted-by":"crossref","unstructured":"Ravishankar H, Thiruvenkadam S, Venkataramani R (2017) Joint deep learning of foreground, background. In: International conference on information processing in medical imaging. Springer, Berlin, pp 622\u2013632","DOI":"10.1007\/978-3-319-59050-9_49"},{"key":"2085_CR10","first-page":"234","volume-title":"Lecture Notes in Computer Science","author":"Olaf Ronneberger","year":"2015","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-Net: convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention. Springer, Berlin, pp 234\u2013241"},{"key":"2085_CR11","doi-asserted-by":"publisher","first-page":"424","DOI":"10.1007\/978-3-319-46723-8_49","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2016","author":"\u00d6zg\u00fcn \u00c7i\u00e7ek","year":"2016","unstructured":"\u00c7i\u00e7ek \u00d6, Abdulkadir A, Lienkamp SS, Brox T, Ronneberger O (2016) 3D U-Net: Learning dense volumetric segmentation from sparse annotation BT\u2014medical image computing and computer-assisted intervention\u2014MICCAI 2016. In: International conference on medical image computing and computer-assisted intervention. Springer, Berlin, pp 424\u2013432"},{"key":"2085_CR12","doi-asserted-by":"publisher","first-page":"460","DOI":"10.1007\/978-3-319-46723-8_53","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2016","author":"A\u00efcha BenTaieb","year":"2016","unstructured":"BenTaieb A, Hamarneh G (2016) Topology aware fully convolutional networks for histology gland segmentation. In: International conference on medical image computing and computer-assisted intervention. Springer, Berlin, pp 460\u2013468"},{"key":"2085_CR13","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1016\/j.media.2016.11.004","volume":"36","author":"H Chen","year":"2017","unstructured":"Chen H, Qi X, Yu L, Dou Q, Qin J, Heng P-A (2017) DCAN: deep contour-aware networks for object instance segmentation from histology images. Med Image Anal 36:135\u2013146","journal-title":"Med Image Anal"},{"key":"2085_CR14","doi-asserted-by":"publisher","unstructured":"Ledig C, Theis L, Husz\u00e1r F, Caballero J, Cunningham A, Acosta A, Aitken A, Tejani A, Totz J, Wang Z, Shi W (2017) Anatomically constrained neural networks (ACNN): application to cardiac image enhancement and segmentation-supplementary. In: Proceedings of the 30th IEEE conference on comput vis pattern recognition, CVPR 2017, pp 105\u2013114. \nhttps:\/\/doi.org\/10.1109\/cvpr.2017.19","DOI":"10.1109\/cvpr.2017.19"},{"key":"2085_CR15","unstructured":"Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. arXiv Prepr \narXiv:150302531"},{"key":"2085_CR16","unstructured":"Wu Z, Song S, Khosla A, Yu F, Zhang L, Tang X, Xiao J (2015) 3d shapenets: a deep representation for volumetric shapes BT. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1912\u20131920"},{"key":"2085_CR17","doi-asserted-by":"crossref","unstructured":"Sharma A, Grau O, Fritz M (2016) Vconv-dae: deep volumetric shape learning without object labels. In: European conference on computer vision. Springer, Berlin, pp 236\u2013250","DOI":"10.1007\/978-3-319-49409-8_20"},{"key":"2085_CR18","doi-asserted-by":"publisher","first-page":"383","DOI":"10.1007\/s12021-018-9377-x","volume":"16","author":"Y Xue","year":"2018","unstructured":"Xue Y, Xu T, Zhang H, Long LR, Huang X (2018) Segan: adversarial network with multi-scale l1 loss for medical image segmentation. Neuroinformatics 16:383\u2013392","journal-title":"Neuroinformatics"},{"key":"2085_CR19","doi-asserted-by":"crossref","unstructured":"Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3431\u20133440","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"2085_CR20","doi-asserted-by":"crossref","unstructured":"Dalca AV, Guttag J, Sabuncu MR (2018) Anatomical priors in convolutional networks for unsupervised biomedical segmentation. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition, pp 9290\u20139299","DOI":"10.1109\/CVPR.2018.00968"},{"key":"2085_CR21","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1007\/978-3-319-66182-7_24","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2212 MICCAI 2017","author":"H. Ravishankar","year":"2017","unstructured":"Ravishankar H, Venkataramani R, Thiruvenkadam S, Sudhakar P, Vaidya V (2017) Learning and incorporating shape models for semantic segmentation. In: Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics)"},{"key":"2085_CR22","unstructured":"Clevert D-A, Unterthiner T, Hochreiter S (2015) Fast and accurate deep network learning by exponential linear units (ELUS). arXiv Prepr \narXiv:151107289"},{"key":"2085_CR23","unstructured":"Pedamonti D (2018) Comparison of non-linear activation functions for deep neural networks on MNIST classification task. arXiv Prepr \narXiv:180402763"},{"key":"2085_CR24","unstructured":"Van Ginneken B, Heimann T, Styner M (2007) MICCAI workshop on 3D segmentation in the clinic: a grand challenge. In: 3D segmentation in the Clinic: a grand challenge, pp 3\u20134"},{"key":"2085_CR25","unstructured":"Soler L, Hostettler A, Agnus V, Charnoz A, Fasquel J, Moreau J, Osswald A, Bouhadjar M, Marescaux J (2010) 3D image reconstruction for comparison of algorithm database: a patient-specific anatomical and medical image database. IRCAD, Strasbourg, Fr. Tech. Rep"},{"key":"2085_CR26","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1007\/s11548-017-1671-9","volume":"13","author":"Y Xu","year":"2018","unstructured":"Xu Y, Lin L, Hu H, Wang D, Zhu W, Wang J, Han XH, Chen YW (2018) Texture-specific bag of visual words model and spatial cone matching-based method for the retrieval of focal liver lesions using multiphase contrast-enhanced CT images. Int J Comput Assist Radiol Surg 13:151\u2013164. \nhttps:\/\/doi.org\/10.1007\/s11548-017-1671-9","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"2085_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2019.01.001","author":"J Wang","year":"2019","unstructured":"Wang J, Li J, Han XH, Lin L, Hu H, Xu Y, Chen Q, Iwamoto Y, Chen YW (2019) Tensor-based sparse representations of multi-phase medical images for classification of focal liver lesions. Pattern Recognit Lett. \nhttps:\/\/doi.org\/10.1016\/j.patrec.2019.01.001","journal-title":"Pattern Recognit Lett"},{"key":"2085_CR28","unstructured":"Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean J, Devin M, Ghemawat S, Irving G, Isard M, Kudlur M, Levenberg J, Monga R, Moore S, Murray DG, Steiner B, Tucker P, Vasudevan V, Warden P, Wicke M, Yu Y, Zheng X (2016) TensorFlow: a system for large-scale machine learning. In: OSDI, pp 265\u2013283"},{"key":"2085_CR29","unstructured":"Kingma D, Ba J (2014) Adam: a method for stochastic optimization. arXiv Prepr \narXiv:14126980"},{"key":"2085_CR30","doi-asserted-by":"publisher","first-page":"46","DOI":"10.5120\/5083-7333","volume":"39","author":"S Gunasundari","year":"2012","unstructured":"Gunasundari S, Suganya Ananthi M (2012) Comparison and evaluation of methods for liver tumor classification from CT datasets. Int J Comput Appl 39:46\u201351. \nhttps:\/\/doi.org\/10.5120\/5083-7333","journal-title":"Int J Comput Appl"},{"key":"2085_CR31","unstructured":"Al-Shaikhli SDS, Yang MY, Rosenhahn B (2015) Automatic 3D liver segmentation using sparse representation of global and local image information via level set formulation. arXiv Prepr \narXiv:150801521"},{"key":"2085_CR32","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1016\/j.compbiomed.2015.10.007","volume":"67","author":"C Dong","year":"2015","unstructured":"Dong C, Chen Y, Foruzan AH, Lin L, Han X, Tateyama T, Wu X, Xu G, Jiang H (2015) Segmentation of liver and spleen based on computational anatomy models. Comput Biol Med 67:146\u2013160","journal-title":"Comput Biol Med"},{"key":"2085_CR33","doi-asserted-by":"publisher","DOI":"10.1186\/s12938-016-0296-5","author":"Y Zheng","year":"2017","unstructured":"Zheng Y, Ai D, Mu J, Cong W, Wang X, Zhao H, Yang J (2017) Automatic liver segmentation based on appearance and context information. Biomed Eng Online. \nhttps:\/\/doi.org\/10.1186\/s12938-016-0296-5","journal-title":"Biomed Eng Online"},{"key":"2085_CR34","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-018-28787-y","author":"X Lu","year":"2018","unstructured":"Lu X, Xie Q, Zha Y, Wang D (2018) Fully automatic liver segmentation combining multi-dimensional graph cut with shape information in 3D CT images. Sci Rep. \nhttps:\/\/doi.org\/10.1038\/s41598-018-28787-y","journal-title":"Sci Rep"}],"container-title":["International Journal of Computer Assisted Radiology and Surgery"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-019-02085-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11548-019-02085-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-019-02085-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,11,3]],"date-time":"2020-11-03T00:30:07Z","timestamp":1604363407000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11548-019-02085-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11,4]]},"references-count":34,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2020,2]]}},"alternative-id":["2085"],"URL":"https:\/\/doi.org\/10.1007\/s11548-019-02085-y","relation":{},"ISSN":["1861-6410","1861-6429"],"issn-type":[{"value":"1861-6410","type":"print"},{"value":"1861-6429","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,11,4]]},"assertion":[{"value":"1 June 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 October 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 November 2019","order":3,"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":"We further confirm that any aspect of the work covered in this manuscript that has involved either experimental animals or human patients has been conducted with the ethical approval of all relevant bodies and that such approvals are acknowledged within the manuscript.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical standard"}}]}}