{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T12:37:25Z","timestamp":1768739845782,"version":"3.49.0"},"reference-count":58,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2017,2,28]],"date-time":"2017-02-28T00:00:00Z","timestamp":1488240000000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"name":"Yildiz Technical University, Scientific Research Projects Coordination Department","award":["2014-04-01-KAP01"],"award-info":[{"award-number":["2014-04-01-KAP01"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2017,10]]},"DOI":"10.1007\/s11517-017-1630-1","type":"journal-article","created":{"date-parts":[[2017,2,28]],"date-time":"2017-02-28T07:59:47Z","timestamp":1488268787000},"page":"1829-1848","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":71,"title":["Cell segmentation in histopathological images with deep learning algorithms by utilizing spatial relationships"],"prefix":"10.1007","volume":"55","author":[{"given":"Nuh","family":"Hatipoglu","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5532-477X","authenticated-orcid":false,"given":"Gokhan","family":"Bilgin","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2017,2,28]]},"reference":[{"issue":"1","key":"1630_CR1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1561\/2200000006","volume":"2","author":"Y Bengio","year":"2009","unstructured":"Bengio Y (2009) Learning deep architectures for AI. Found Trend Mach Learn 2(1):1\u2013127","journal-title":"Found Trend Mach Learn"},{"key":"1630_CR2","doi-asserted-by":"crossref","unstructured":"Bilgin G (2013) Evaluation of spatial relations in the segmentation of histopathological images. In: IEEE 21st signal processing and communications applications conference, pp 1\u20134","DOI":"10.1109\/SIU.2013.6531182"},{"key":"1630_CR3","doi-asserted-by":"crossref","unstructured":"Bunyak F, Hafiane A, Palaniappan K (2011) Histopathology tissue segmentation by combining fuzzy clustering with multiphase vector level sets. In: Arabnia HR, Tran Q-N (eds) Software tools and algorithms for biological systems. Springer, Berlin, pp 413\u2013424","DOI":"10.1007\/978-1-4419-7046-6_41"},{"issue":"3","key":"1630_CR4","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1145\/1961189.1961199","volume":"2","author":"CC Chang","year":"2011","unstructured":"Chang CC, Lin CJ (2011) Libsvm: a library for support vector machines. ACM Trans Intell Syst Technol 2(3):27","journal-title":"ACM Trans Intell Syst Technol"},{"issue":"6","key":"1630_CR5","first-page":"2094","volume":"7","author":"Y Chen","year":"2014","unstructured":"Chen Y, Lin Z, Zhao X, Wang G, Gu Y (2014) Deep learning-based classification of hyperspectral data. IEEE J Sel Top Appl 7(6):2094\u20132107","journal-title":"IEEE J Sel Top Appl"},{"key":"1630_CR6","doi-asserted-by":"crossref","unstructured":"Cheng L, Ye N, Yu W, Cheah A (2012) A bag-of-words model for cellular image segmentation. In: Lom\u00e9nie N, Racoceanu D, Gouaillard A (eds) Advances in bio-imaging: from physics to signal understanding issues. Springer, Berlin, pp 209\u2013222","DOI":"10.1007\/978-3-642-25547-2_13"},{"key":"1630_CR7","unstructured":"Chollet F (2015) Keras. http:\/\/github.com\/fchollet\/keras"},{"key":"1630_CR8","doi-asserted-by":"crossref","unstructured":"Ciresan DC, Meier U, Gambardella LM, Schmidhuber J (2011) Convolutional neural network committees for handwritten character classification. In: IEEE international conference on document analysis and recognition, ICDAR\u201911, pp 1135\u20131139","DOI":"10.1109\/ICDAR.2011.229"},{"key":"1630_CR9","doi-asserted-by":"crossref","unstructured":"Ciresan DC, Giusti A, Gambardella LM, Schmidhuber J (2013) Mitosis detection in breast cancer histology images with deep neural networks. In: 16th International conference of medical image computing and computer-assisted intervention (MICCAI\u201913). Springer, Berlin, pp 411\u2013418","DOI":"10.1007\/978-3-642-40763-5_51"},{"issue":"2","key":"1630_CR10","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.artmed.2011.04.010","volume":"52","author":"A Cruz-Roa","year":"2011","unstructured":"Cruz-Roa A, Caicedo JC, Gonzalez FA (2011) Visual pattern mining in histology image collections using bag of features. Artif Intell Med 52(2):91\u2013106","journal-title":"Artif Intell Med"},{"key":"1630_CR11","doi-asserted-by":"crossref","unstructured":"Cruz-Roa A, Xu J, Madabhushi A (2015) A note on the stability and discriminability of graph-based features for classification problems in digital pathology. In: Proceedings of the SPIE, vol 9287, pp 928703\u2013928710","DOI":"10.1117\/12.2085141"},{"key":"1630_CR12","unstructured":"Cun L, Boser B, Denker JS, Henderson D, Howard RE, Hubbard W, Jackel LD (1990) Handwritten digit recognition with a back-propagation network. In: Touretzky DS (ed) Advances in neural information processing systems. Morgan Kaufmann, Los Altos, pp 396\u2013404"},{"key":"1630_CR13","unstructured":"Demir C, Yener B (2005) Automated cancer diagnosis based on histopathological images: a systematic survey. Rensselaer Polytechnic Institute, Technical Report"},{"issue":"3\u20134","key":"1630_CR14","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1561\/2000000039","volume":"7","author":"L Deng","year":"2014","unstructured":"Deng L, Yu D (2014) Deep learning: methods and applications. Found Trend Signal Process 7(3\u20134):197\u2013387","journal-title":"Found Trend Signal Process"},{"issue":"7","key":"1630_CR15","doi-asserted-by":"crossref","first-page":"1977","DOI":"10.1109\/TBME.2011.2110648","volume":"58","author":"MM Dundar","year":"2011","unstructured":"Dundar MM, Badve S, Bilgin G, Raykar V, Jain R, Sertel O, Gurcan MN (2011) Computerized classification of intraductal breast lesions using histopathological images. IEEE Trans Biomed Eng 58(7):1977\u20131984","journal-title":"IEEE Trans Biomed Eng"},{"issue":"4","key":"1630_CR16","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1007\/BF00344251","volume":"36","author":"K Fukushima","year":"1980","unstructured":"Fukushima K (1980) Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol Cybern 36(4):193\u2013202","journal-title":"Biol Cybern"},{"issue":"1","key":"1630_CR17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1471-2105-10-1","volume":"10","author":"ED Gelasca","year":"2009","unstructured":"Gelasca ED, Obara B, Fedorov D, Kvilekval K, Manjunath B (2009) A biosegmentation benchmark for evaluation of bioimage analysis methods. BMC Bioinform 10(1):1","journal-title":"BMC Bioinform"},{"issue":"12","key":"1630_CR18","doi-asserted-by":"crossref","first-page":"4151","DOI":"10.1016\/j.patcog.2012.05.006","volume":"45","author":"A Gen\u00e7tav","year":"2012","unstructured":"Gen\u00e7tav A, Aksoy S, \u00d6nder S (2012) Unsupervised segmentation and classification of cervical cell images. Pattern Recognit 45(12):4151\u20134168","journal-title":"Pattern Recognit"},{"key":"1630_CR19","unstructured":"Goodfellow I, Lee H, Le QV, Saxe A, Ng AY (2009) Measuring invariances in deep networks. In: Advances in neural information processing systems, NIPS\u201909, pp 646\u2013654"},{"key":"1630_CR20","unstructured":"Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT Press, Cambridge (in preparation). http:\/\/www.deeplearningbook.org"},{"key":"1630_CR21","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1109\/RBME.2009.2034865","volume":"2","author":"MN Gurcan","year":"2009","unstructured":"Gurcan MN, Boucheron LE, Can A, Madabhushi A, Rajpoot NM, Yener B (2009) Histopathological image analysis: a review. IEEE Rev Biomed Eng 2:147\u2013171","journal-title":"IEEE Rev Biomed Eng"},{"key":"1630_CR22","doi-asserted-by":"crossref","unstructured":"Hatipoglu N, Bilgin G (2014) Classification of histopathological images using convolutional neural network. In: IEEE 4th international conference on image processing theory, tools and applications, IPTA\u201914, pp 1\u20136","DOI":"10.1109\/IPTA.2014.7001976"},{"key":"1630_CR23","doi-asserted-by":"crossref","unstructured":"Hatipoglu N, Bilgin G (2015) Segmentation of histopathological images with convolutional neural networks using fourier features. In: IEEE 23th signal processing and communications applications conference, SIU\u20192015, pp 455\u2013458","DOI":"10.1109\/SIU.2015.7129857"},{"issue":"3","key":"1630_CR24","doi-asserted-by":"crossref","first-page":"538","DOI":"10.1016\/j.cmpb.2011.12.007","volume":"107","author":"L He","year":"2012","unstructured":"He L, Long LR, Antani S, Thoma GR (2012) Histology image analysis for carcinoma detection and grading. Comput Meth Prog Biol 107(3):538\u2013556","journal-title":"Comput Meth Prog Biol"},{"issue":"5786","key":"1630_CR25","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","volume":"313","author":"GE Hinton","year":"2006","unstructured":"Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313(5786):504\u2013507","journal-title":"Science"},{"key":"1630_CR26","doi-asserted-by":"crossref","unstructured":"Irshad H, Montaser-Kouhsari L, Waltz G, Bucur O, Nowak J, Dong F, Knoblauch NW, Beck AH (2014) Crowdsourcing image annotation for nucleus detection and segmentation in computational pathology: evaluating experts, automated methods, and the crowd. In: Pacific symposium on biocomputing, PSB\u201915. NIH Public Access, pp 294\u2013305","DOI":"10.1142\/9789814644730_0029"},{"key":"1630_CR27","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1109\/RBME.2013.2295804","volume":"7","author":"H Irshad","year":"2014","unstructured":"Irshad H, Veillard A, Roux L, Racoceanu D (2014) Methods for nuclei detection, segmentation, and classification in digital histopathology: A review\u2014current status and future potential. IEEE Rev Biomed Eng 7:97\u2013114","journal-title":"IEEE Rev Biomed Eng"},{"key":"1630_CR28","unstructured":"Jaiantilal A (2009) Classification and regression by randomforest-matlab. http:\/\/code.google.com\/p\/randomforest-matlab"},{"key":"1630_CR29","doi-asserted-by":"publisher","unstructured":"Jothi JAA, Rajam VMA (2016) A survey on automated cancer diagnosis from histopathology images. Artif Intell Rev. doi: 10.1007\/s10462-016-9494-6","DOI":"10.1007\/s10462-016-9494-6"},{"key":"1630_CR30","doi-asserted-by":"crossref","unstructured":"Karakis R, Tez M, Guler I (2011) Classification the axillary lymph node status of breast cancer patients with the analysis of pattern recognition. In: IEEE 19th conference on signal processing and communications applications, SIU\u20192011, pp 988\u2013991","DOI":"10.1109\/SIU.2011.5929819"},{"issue":"3","key":"1630_CR31","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1007\/s10278-008-9129-9","volume":"22","author":"B Ko","year":"2009","unstructured":"Ko B, Seo M, Nam JY (2009) Microscopic cell nuclei segmentation based on adaptive attention window. J Digit Imaging 22(3):259\u2013274","journal-title":"J Digit Imaging"},{"issue":"4","key":"1630_CR32","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1007\/s11517-015-1361-0","volume":"54","author":"SA Korkmaz","year":"2016","unstructured":"Korkmaz SA, Korkmaz MF, Poyraz M (2016) Diagnosis of breast cancer in light microscopic and mammographic images textures using relative entropy via kernel estimation. Med Biol Eng Comput 54(4):561\u2013573","journal-title":"Med Biol Eng Comput"},{"key":"1630_CR33","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, NIPS\u201912, pp 1097\u20131105"},{"issue":"6","key":"1630_CR34","doi-asserted-by":"crossref","first-page":"2955","DOI":"10.1109\/TIP.2012.2187670","volume":"21","author":"YN Law","year":"2012","unstructured":"Law YN, Lee HK, Ng MK, Yip AM (2012) A semisupervised segmentation model for collections of images. IEEE Trans Image Process 21(6):2955\u20132968","journal-title":"IEEE Trans Image Process"},{"issue":"11","key":"1630_CR35","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278\u20132324","journal-title":"Proc IEEE"},{"key":"1630_CR36","doi-asserted-by":"crossref","unstructured":"LeCun Y, Kavukcuoglu K, Farabet C, et\u00a0al (2010) Convolutional networks and applications in vision. In: IEEE international symposium on circuits and systems, ISCAS\u201910, pp 253\u2013256","DOI":"10.1109\/ISCAS.2010.5537907"},{"issue":"7553","key":"1630_CR37","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436\u2013444","journal-title":"Nature"},{"issue":"11","key":"1630_CR38","doi-asserted-by":"crossref","first-page":"1049","DOI":"10.1109\/42.816070","volume":"18","author":"TM Lehmann","year":"1999","unstructured":"Lehmann TM, Gonner C, Spitzer K (1999) Survey: interpolation methods in medical image processing. IEEE Trans Med Imaging 18(11):1049\u20131075","journal-title":"IEEE Trans Med Imaging"},{"issue":"1","key":"1630_CR39","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1111\/j.1365-2818.2008.02016.x","volume":"231","author":"G Li","year":"2008","unstructured":"Li G, Liu T, Nie J, Guo L, Chen J, Zhu J, Xia W, Mara A, Holley S, Wong S (2008) Segmentation of touching cell nuclei using gradient flow tracking. J Microsc 231(1):47\u201358","journal-title":"J Microsc"},{"key":"1630_CR40","doi-asserted-by":"crossref","unstructured":"Li X, Plataniotis KN (2015) Color model comparative analysis for breast cancer diagnosis using H&E stained images. In: SPIE medical imaging, international society for optics and photonics, p 94200L","DOI":"10.1117\/12.2079935"},{"key":"1630_CR41","doi-asserted-by":"crossref","unstructured":"Naik S, Doyle S, Agner S, Madabhushi A, Feldman M, Tomaszewski J (2008) Automated gland and nuclei segmentation for grading of prostate and breast cancer histopathology. In: IEEE 5th international symposium on biomedical imaging: from nano to macro, ISBI\u20192008, pp 284\u2013287","DOI":"10.1109\/ISBI.2008.4540988"},{"key":"1630_CR42","unstructured":"Ng A, Ngiam J, Foo CY, Mai Y, Suen C (2012) Unsupervised feature learning and deep learning tutorial (online). Accessed 2016-01-07"},{"issue":"1","key":"1630_CR43","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1746-1596-8-1","volume":"8","author":"V Ojansivu","year":"2013","unstructured":"Ojansivu V, Linder N, Rahtu E, Pietik\u00e4inen M, Lundin M, Joensuu H, Lundin J (2013) Automated classification of breast cancer morphology in histopathological images. Diagn Pathol 8(1):1\u20134","journal-title":"Diagn Pathol"},{"key":"1630_CR44","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.micron.2013.01.003","volume":"47","author":"D Onder","year":"2013","unstructured":"Onder D, Sarioglu S, Karacali B (2013) Automated labelling of cancer textures in colorectal histopathology slides using quasi-supervised learning. Micron 47:33\u201342","journal-title":"Micron"},{"key":"1630_CR45","doi-asserted-by":"crossref","unstructured":"Oquab M, Bottou L, Laptev I, Sivic J (2015) Is object localization for free?-weakly-supervised learning with convolutional neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, CVPR\u201915, pp 685\u2013694","DOI":"10.1109\/CVPR.2015.7298668"},{"key":"1630_CR46","unstructured":"Palm RB (2012) Prediction as a candidate for learning deep hierarchical models of data. Master\u2019s thesis, Technical University of Denmark, Palm"},{"key":"1630_CR47","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1016\/j.neucom.2016.08.103","volume":"229","author":"X Pan","year":"2017","unstructured":"Pan X, Li L, Yang H, Liu Z, Yang J, Zhao L, Fan Y (2017) Accurate segmentation of nuclei in pathological images via sparse reconstruction and deep convolutional networks. Neurocomputing 229:88\u201399. doi: 10.1016\/j.neucom.2016.08.103","journal-title":"Neurocomputing"},{"key":"1630_CR48","doi-asserted-by":"crossref","unstructured":"Petersen K, Nielsen M, Diao P, Karssemeijer N, Lillholm M (2014) Breast tissue segmentation and mammographic risk scoring using deep learning. In: Breast imaging: proceedings of 12th international workshop on digital mammography, IWDM\u201914. Springer, Berlin, pp 88\u201394","DOI":"10.1007\/978-3-319-07887-8_13"},{"key":"1630_CR49","unstructured":"Phung SL, Bouzerdoum A (2009) Matlab library for convolutional neural networks. Tech. rep., ICT Research Institute, Visual and Audio Signal Processing Laboratory, University of Wollongong"},{"key":"1630_CR50","unstructured":"Poultney C, Chopra S, Cun YL, et\u00a0al (2006) Efficient learning of sparse representations with an energy-based model. In: Advances in neural information processing systems, NIPS\u201906, pp 1137\u20131144"},{"issue":"5","key":"1630_CR51","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1007\/s11517-006-0044-2","volume":"44","author":"NE Ross","year":"2006","unstructured":"Ross NE, Pritchard CJ, Rubin DM, Dus\u00e9 AG (2006) Automated image processing method for the diagnosis and classification of malaria on thin blood smears. Med Biol Eng Comput 44(5):427\u2013436","journal-title":"Med Biol Eng Comput"},{"issue":"12","key":"1630_CR52","doi-asserted-by":"crossref","first-page":"1041","DOI":"10.1007\/s11517-014-1200-8","volume":"52","author":"M Saraswat","year":"2014","unstructured":"Saraswat M, Arya KV (2014) Feature selection and classification of leukocytes using random forest. Med Biol Eng Comput 52(12):1041\u20131052","journal-title":"Med Biol Eng Comput"},{"issue":"7","key":"1630_CR53","doi-asserted-by":"crossref","first-page":"e70,221","DOI":"10.1371\/journal.pone.0070221","volume":"8","author":"M Veta","year":"2013","unstructured":"Veta M, van Diest PJ, Kornegoor R, Huisman A, Viergever MA, Pluim JP (2013) Automatic nuclei segmentation in H&E stained breast cancer histopathology images. PloS ONE 8(7):e70,221","journal-title":"PloS ONE"},{"issue":"5","key":"1630_CR54","doi-asserted-by":"crossref","first-page":"1400","DOI":"10.1109\/TBME.2014.2303852","volume":"61","author":"M Veta","year":"2014","unstructured":"Veta M, Pluim JP, van Diest PJ, Viergever MA (2014) Breast cancer histopathology image analysis: a review. IEEE Trans Biomed Eng 61(5):1400\u20131411","journal-title":"IEEE Trans Biomed Eng"},{"key":"1630_CR55","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1038\/srep00503","volume":"2","author":"S Wienert","year":"2012","unstructured":"Wienert S, Heim D, Saeger K, Stenzinger A, Beil M, Hufnagl P, Dietel M, Denkert C, Klauschen F (2012) Detection and segmentation of cell nuclei in virtual microscopy images: a minimum-model approach. Sci Rep 2:503","journal-title":"Sci Rep"},{"issue":"4","key":"1630_CR56","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1055\/s-0038-1633889","volume":"43","author":"T Wittenberg","year":"2004","unstructured":"Wittenberg T, Grobe M, Munzenmayer C, Kuziela H, Spinnler K (2004) A semantic approach to segmentation of overlapping objects. Method Inform Med 43(4):343\u2013353","journal-title":"Method Inform Med"},{"key":"1630_CR57","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1016\/j.neucom.2016.01.034","volume":"191","author":"J Xu","year":"2016","unstructured":"Xu J, Luo X, Wang G, Gilmore H, Madabhushi A (2016) A deep convolutional neural network for segmenting and classifying epithelial and stromal regions in histopathological images. Neurocomputing 191:214\u2013223","journal-title":"Neurocomputing"},{"issue":"3","key":"1630_CR58","doi-asserted-by":"crossref","first-page":"591","DOI":"10.1016\/j.media.2014.01.010","volume":"18","author":"Y Xu","year":"2014","unstructured":"Xu Y, Zhu JY, Eric I, Chang C, Lai M, Tu Z (2014) Weakly supervised histopathology cancer image segmentation and classification. Med Image Anal 18(3):591\u2013604","journal-title":"Med Image Anal"}],"container-title":["Medical &amp; Biological Engineering &amp; Computing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11517-017-1630-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-017-1630-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-017-1630-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,24]],"date-time":"2022-07-24T22:40:03Z","timestamp":1658702403000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11517-017-1630-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,2,28]]},"references-count":58,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2017,10]]}},"alternative-id":["1630"],"URL":"https:\/\/doi.org\/10.1007\/s11517-017-1630-1","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"value":"0140-0118","type":"print"},{"value":"1741-0444","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,2,28]]}}}