{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,16]],"date-time":"2025-07-16T13:55:07Z","timestamp":1752674107301},"reference-count":53,"publisher":"Springer Science and Business Media LLC","issue":"7-8","license":[{"start":{"date-parts":[[2021,7,17]],"date-time":"2021-07-17T00:00:00Z","timestamp":1626480000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,7,17]],"date-time":"2021-07-17T00:00:00Z","timestamp":1626480000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2021,8]]},"DOI":"10.1007\/s11517-021-02367-1","type":"journal-article","created":{"date-parts":[[2021,7,17]],"date-time":"2021-07-17T07:03:01Z","timestamp":1626505381000},"page":"1659-1672","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["B-Map: a fuzzy-based model to detect foreign objects in a brain"],"prefix":"10.1007","volume":"59","author":[{"given":"Dev","family":"Baloni","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shashi Kant","family":"Verma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,7,17]]},"reference":[{"key":"2367_CR1","doi-asserted-by":"publisher","unstructured":"Hachemi B, Oudjemia S, Alim F, Seddiki S, Talbi F, Abdelaziz M (2015) \"Cerebralabnormalities detection by region-growing segmentation and KNNclassification,\" Fifth International Conference on theInnovative Computing Technology (INTECH 2015), pp 23\u201326. https:\/\/doi.org\/10.1109\/INTECH.2015.7173371","DOI":"10.1109\/INTECH.2015.7173371"},{"issue":"3","key":"2367_CR2","first-page":"807","volume":"35","author":"S Chen","year":"2017","unstructured":"Chen S et al (2017) Low-Level Segmentation of 3-D magnetic resonance brain images-a rule-based system. Med Image Anal 35(3):807\u2013810","journal-title":"Med Image Anal"},{"issue":"Iccmc","key":"2367_CR3","first-page":"1131","volume":"57","author":"W El Hajj Chehade","year":"2014","unstructured":"El Hajj Chehade W et al (2014) Automatic left ventricle segmentation using iterative thresholding and an active contour model with adaptation on short-axis cardiac MRI. Proc World Congr Intell Control Autom 57(Iccmc):1131\u20131136","journal-title":"Proc World Congr Intell Control Autom"},{"issue":"4","key":"2367_CR4","doi-asserted-by":"publisher","first-page":"840","DOI":"10.1109\/TMI.2017.2737232","volume":"37","author":"CJ Daniels","year":"2018","unstructured":"Daniels CJ, Gallagher FA (2018) Unsupervised segmentation of 5D hyperpolarized carbon-13 MRI data using a Fuzzy Markov random field model. IEEE Trans Med Imaging 37(4):840\u2013850","journal-title":"IEEE Trans Med Imaging"},{"key":"2367_CR5","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1016\/j.patcog.2018.11.009","volume":"88","author":"S Chen","year":"2019","unstructured":"Chen S, Ding C, Liu M (2019) Dual-force convolutional neural networks for accurate brain tumor segmentation. Pattern Recognit 88:90\u2013100","journal-title":"Pattern Recognit"},{"key":"2367_CR6","doi-asserted-by":"publisher","unstructured":"Singh G, Ansari MA (2017) Efficient detection of brain tumor from MRIs using K-means segmentation and normalized histogram. 20161st India International Conference on Information Processing (IICIP), pp 1\u20136. https:\/\/doi.org\/10.1109\/IICIP.2016.7975365","DOI":"10.1109\/IICIP.2016.7975365"},{"key":"2367_CR7","first-page":"1","volume":"2018","author":"E Hossain","year":"2019","unstructured":"Hossain E, Hossain MF, Rahaman MA (2019) \u201cAn approach for the detection and classification of tumor cells from bone MRI using wavelet transform and KNN classifier\u201d, 2018 Int. Conf Innov Eng Technol ICIET 2018:1\u20136","journal-title":"Conf Innov Eng Technol ICIET"},{"key":"2367_CR8","doi-asserted-by":"publisher","unstructured":"Ardon R, Cohen LD, Yezzi A (2007) ANew Implicit Method for Surface Segmentation by Minimal Paths:Applications in 3D Medical Images. In: Rangarajan A, Vemuri B, Yuille AL (eds) Energy Minimization Methods in Computer Vision andPattern Recognition. EMMCVPR 2005. Lecture Notes in Computer Science,vol 3757. Springer, Berlin, Heidelberg. https:\/\/doi.org\/10.1007\/11585978_34","DOI":"10.1007\/11585978_34"},{"issue":"3\u20134","key":"2367_CR9","doi-asserted-by":"publisher","first-page":"888","DOI":"10.1016\/j.mcm.2010.11.012","volume":"54","author":"C Wang","year":"2011","unstructured":"Wang C, Li X, Wang W, Feng Y, Zhou Z, Zhan H (2011) Recognition of worm-eaten chestnuts based on machine vision. Math Comput Model 54(3\u20134):888\u2013894","journal-title":"Math Comput Model"},{"key":"2367_CR10","doi-asserted-by":"publisher","unstructured":"Batlle J, Casals A, Freixenet J (2000) A review on strategies for recognizing natural objects in colour images of outdoor scenes. 18:515\u2013530.\u00a0https:\/\/doi.org\/10.1016\/S0262-8856(99)00040-2","DOI":"10.1016\/S0262-8856(99)00040-2"},{"issue":"1","key":"2367_CR11","doi-asserted-by":"publisher","first-page":"3","DOI":"10.2174\/157340561101150423103441","volume":"11","author":"S Masood","year":"2015","unstructured":"Masood S, Sharif M, Masood A, Yasmin M, Raza M (2015) A survey on medical image segmentation. Curr Med Imaging Rev 11(1):3\u201314","journal-title":"Curr Med Imaging Rev"},{"key":"2367_CR12","doi-asserted-by":"publisher","unstructured":"Khan MW (2014) A survey\u202f: image segmentation techniques.\u00a0Int J Image\u00a0Graph 1:166\u2013170. https:\/\/doi.org\/10.12720\/joig.1.4.166-170","DOI":"10.12720\/joig.1.4.166-170"},{"issue":"1\u20132","key":"2367_CR13","first-page":"35","volume":"1","author":"PP Ray","year":"2017","unstructured":"Ray PP (2017) A survey of IoT cloud platforms. Futur Comput Informatics J 1(1\u20132):35\u201346","journal-title":"Futur Comput Informatics J"},{"issue":"3","key":"2367_CR14","first-page":"801","volume":"4","author":"H Steinhaus","year":"1956","unstructured":"Steinhaus H (1956) Sur la division des corps materiels en parties. Bull Polish Acad Sci 4(3):801\u2013804","journal-title":"Bull Polish Acad Sci"},{"key":"2367_CR15","doi-asserted-by":"crossref","unstructured":"Singh M, Patel P, Khosla D, Kim T \u201cSegmentation of functional MRI by K-means clustering,\u201d in 1995 IEEE Nuclear Science Symposium and Medical Imaging Conference Record 3:1732\u20131736","DOI":"10.1109\/23.507264"},{"issue":"600","key":"2367_CR16","first-page":"60","volume":"3","author":"SN Sulaiman","year":"2017","unstructured":"Sulaiman SN, Non NA, Isa IS, Hamzah N (2017) \u201cSegmentation of brain MRI image based on clustering algorithm\u201d, ISIEA 2014\u20132014 IEEE Symp. Ind Electron Appl 3(600):60\u201365","journal-title":"Ind Electron Appl"},{"key":"2367_CR17","doi-asserted-by":"publisher","unstructured":"El Hajj Chehade W, Kader RA, El-Zaart A (2018) \u201cSegmentation of MRI images for brain cancer detection,\u201d 2018 Int. Conf. Inf. Commun. Technol. ICOIACT 2018, vol. 2018-Janua, pp. 929\u2013934.\u00a0https:\/\/doi.org\/10.1109\/ICOIACT.2018.8350721","DOI":"10.1109\/ICOIACT.2018.8350721"},{"key":"2367_CR18","doi-asserted-by":"publisher","unstructured":"Katz, Merickel (2003) \u201cTranslation-invariant aorta segmentation from magnetic resonance images,\u201d International1989 Joint Conference on Neural Networks, 1989 1:327\u2013333. https:\/\/doi.org\/10.1109\/IJCNN.1989.118604","DOI":"10.1109\/IJCNN.1989.118604"},{"key":"2367_CR19","doi-asserted-by":"publisher","unstructured":"Joseph N, Sanghani P, Ren H (2017) \u201cBrain MRI using machine learning techniques,\u201d\u00a016thIEEE International Conference on Machine Learning and Applications(ICMLA), 2017, pp 1149\u20131152. https:\/\/doi.org\/10.1109\/ICMLA.2017.00017","DOI":"10.1109\/ICMLA.2017.00017"},{"key":"2367_CR20","doi-asserted-by":"publisher","unstructured":"Sezgin (2004) \u201cSurvey over image thresholding techniques and quantitative performance evaluation,\u201d 13(1):146\u2013165.\u00a0https:\/\/doi.org\/10.1117\/1.1631315","DOI":"10.1117\/1.1631315"},{"issue":"4","key":"2367_CR21","doi-asserted-by":"publisher","first-page":"905","DOI":"10.1109\/TBME.2009.2014545","volume":"57","author":"HY Lee","year":"2010","unstructured":"Lee HY, Codella NCF, Cham MD, Weinsaft JW, Wang Y (2010) Automatic left ventricle segmentation using iterative thresholding and an active contour model with adaptation on short-axis cardiac MRI. IEEE Trans Biomed Eng 57(4):905\u2013913","journal-title":"IEEE Trans Biomed Eng"},{"key":"2367_CR22","doi-asserted-by":"publisher","unstructured":"Zhao W, Wang L, Shi Y, Xi X, Yin Y, Tang Y (2017) \u201cA multi-objective framework for brain MRI threshold segmentation,\u201d Proc. - 2016 8th Int. Conf. Inf. Technol. Med. Educ. ITME 2016,\u00a0pp. 20\u201324.\u00a0https:\/\/doi.org\/10.1109\/ITME.2016.0015","DOI":"10.1109\/ITME.2016.0015"},{"key":"2367_CR23","doi-asserted-by":"publisher","unstructured":"Salwe S, Raut R, Hajare P (2017) \u201cBrain tumor pixels detection using adaptive wavelet based histogram thresholding and fine windowing,\u201d 2016 Int. Conf. Inf. Technol. InCITe 2016 - Next Gener. IT Summit Theme - Internet Things Connect your Worlds, pp. 256\u2013260.\u00a0https:\/\/doi.org\/10.1109\/INCITE.2016.7857627","DOI":"10.1109\/INCITE.2016.7857627"},{"key":"2367_CR24","doi-asserted-by":"publisher","unstructured":"Fenshia Singh J, Magudeeswaran V (2017) \u201cThresholding based method for segmentation of MRI brain images,\u201d Proc. Int. Conf. IoT Soc. Mobile, Anal. Cloud, I-SMAC 2017, pp. 280\u2013283.\u00a0https:\/\/doi.org\/10.1109\/I-SMAC.2017.8058355","DOI":"10.1109\/I-SMAC.2017.8058355"},{"key":"2367_CR25","unstructured":"Boyce JF (1991) \u201cSegmentation of MR images using neural nets co-occurrence matrices\u201d\u00a0IEEColloquium on Image Processing in Medicine, pp. 5\/1\u20135\/4"},{"key":"2367_CR26","unstructured":"Amin SE, Mageed MA (2012) Brain tumor diagnosis systems based on artificial neural networks and segmentation using MRI.\u00a020128th International Conference on Informatics and Systems (INFOS), pp. MM-119-MM-124"},{"key":"2367_CR27","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.media.2016.05.004","volume":"35","author":"M Havaei","year":"2017","unstructured":"Havaei M et al (2017) Brain tumor segmentation with deep neural networks. Med Image Anal 35:18\u201331","journal-title":"Med Image Anal"},{"key":"2367_CR28","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1016\/j.compbiomed.2019.03.014","volume":"108","author":"H Li","year":"2019","unstructured":"Li H, Li A, Wang M (2019) A novel end-to-end brain tumor segmentation method using improved fully convolutional networks. Comput Biol Med 108:150\u2013160","journal-title":"Comput Biol Med"},{"key":"2367_CR29","doi-asserted-by":"publisher","unstructured":"Jodoin MHP, Larochelle H (2014) \u201cEfficient interactive brain tumor segmentation as within-brain kNN classification,\u201d pp. 556\u2013561.\u00a0https:\/\/doi.org\/10.1109\/ICPR.2014.106","DOI":"10.1109\/ICPR.2014.106"},{"issue":"2","key":"2367_CR30","first-page":"313","volume":"1","author":"Z Peng","year":"2005","unstructured":"Peng Z, Wee W, Lee JH (2005) Mr brain imaging segmentation based on spatial Gaussian mixture model and Markov random field. Proc Int Conf Image Process ICIP 1(2):313\u2013316","journal-title":"Proc Int Conf Image Process ICIP"},{"key":"2367_CR31","doi-asserted-by":"publisher","unstructured":"Bricq S, Collet C, France F-I (2008) \u201c3D Brain MRI segmentation based on robust hidden Markov chain Universit \u00b4 e Strasbourg I LSIIT - UMR CNRS 7005 P\u02c6 ole API , Bd S . Brant , Universit \u00b4 e Strasbourg I LINC - UMR CNRS 7191 4 rue Kirschleger F-67205 Strasbourg - France,\u201d Magn. Reson. Imaging, pp. 517\u2013520.\u00a0https:\/\/doi.org\/10.1109\/ICASSP.2008.4517660","DOI":"10.1109\/ICASSP.2008.4517660"},{"key":"2367_CR32","unstructured":"Bauer S, Nolte L, Reyes M (2018)\u201cSegmentation of brain tumor images based on Atlas-registration combined with a Markov-random-field lesion growth model ,\u201d pp. 2018\u20132021"},{"key":"2367_CR33","doi-asserted-by":"crossref","unstructured":"Peis I et al (2017) \u201cMRI brain segmentation using hidden Markov random fields with alpha-stable distributions,\u201d 2016 IEEE Nucl. Sci. Symp. Med. Imaging Conf. Room-Temperature Semicond. Detect. Work. NSS\/MIC\/RTSD 2016, vol. 2017-Janua, no. 6","DOI":"10.1109\/NSSMIC.2016.8069422"},{"key":"2367_CR34","doi-asserted-by":"publisher","unstructured":"Di Shi W, Wei Y (2012) \u201cA brain segmentation algorithm based on Markov model fused with fuzzy similarity dynamic weights,\u201d Proc. 2012 24th Chinese Control Decis. Conf. CCDC 2012, pp. 1461\u20131464.\u00a0https:\/\/doi.org\/10.1109\/CCDC.2012.6244234","DOI":"10.1109\/CCDC.2012.6244234"},{"key":"2367_CR35","unstructured":"Scrobot\u0103 I, G B\u0103ciu\u0163, Filip AG, Todor B, Blaga F, MF B\u0103ciu\u0163 (2017) \u201cApplication of fuzzy logic in oral cancer risk assessment,\u201d vol. 46, no. 5, pp. 612\u2013619"},{"key":"2367_CR36","doi-asserted-by":"crossref","unstructured":"Bal A, Banerjee M, Chakrabarti A, Sharma P (2018) \u201cMRI brain tumor segmentation and analysis using rough-Fuzzy C-means and shape based properties,\u201d J. King Saud Univ. - Comput. Inf. Sci., no. xxxx, pp. 1\u201319","DOI":"10.1016\/j.jksuci.2018.11.001"},{"key":"2367_CR37","unstructured":"Yang Y (2008) \u201cModified fuzzy multi-thresholding algorithm for segmentation of MRI,\u201d Proc World Congr Intell Control Autom 1131\u20131136"},{"key":"2367_CR38","doi-asserted-by":"crossref","unstructured":"Karnan M, Gopal NN (2010) \u201cHybrid Markov random field with parallel ant colony optimization and Fuzzy C means for MRI brain image segmentation,\u201d 2010 IEEE Int. Conf. Comput. Intell. Comput. Res. ICCIC 2010, pp. 718\u2013721","DOI":"10.1109\/ICCIC.2010.5705896"},{"key":"2367_CR39","doi-asserted-by":"publisher","unstructured":"Abdulbaqi HS, Jafri MZM, Omar AF, Bin Mustafa IS, Abood LK (2014) \u201cDetecting brain tumor in magnetic resonance images using hidden Markov random fields and threshold techniques,\u201d 2014 IEEE Student Conf. Res. Dev. SCOReD 2014, pp. 1\u20135.\u00a0https:\/\/doi.org\/10.1109\/SCORED.2014.7072963","DOI":"10.1109\/SCORED.2014.7072963"},{"issue":"1","key":"2367_CR40","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1504\/IJBET.2014.059060","volume":"14","author":"B Sayah","year":"2014","unstructured":"Sayah B, Tighiouart B (2014) Brain tumour segmentation in MRI: knowledge-based system and region growing approach. Int J Biomed Eng Technol 14(1):71","journal-title":"Int J Biomed Eng Technol"},{"key":"2367_CR41","doi-asserted-by":"crossref","unstructured":"Nandi A (2016) \u201cDetection of human brain tumour using MRI image segmentation and morphological operators,\u201d 2015 IEEE Int. Conf. Comput. Graph. Vis. Inf. Secur. CGVIS 2015, pp. 55\u201360","DOI":"10.1109\/CGVIS.2015.7449892"},{"key":"2367_CR42","doi-asserted-by":"crossref","unstructured":"Liu J, Guo L (2015) \u201cA new brain MRI image segmentation strategy based on K-means clustering and SVM,\u201d Proc. - 2015 7th Int. Conf. Intell. Human-Machine Syst. Cybern. IHMSC 2015, vol. 2, pp. 270\u2013273","DOI":"10.1109\/IHMSC.2015.182"},{"key":"2367_CR43","doi-asserted-by":"publisher","unstructured":"Pidchayathanakorn P, Supratid S (2015) \u201cA hybrid of stationary wavelet thresholding and wiener filtering preprocess for noisy brain MRI spatial fuzzy segmentation,\u201d ECTI-CON 2015 - 2015 12th International Conference on Electrical Engineering\/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), IEEE,\u00a0 Hua Hin, pp 1\u20136. https:\/\/doi.org\/10.1109\/ECTICon.2015.720709","DOI":"10.1109\/ECTICon.2015.720709"},{"key":"2367_CR44","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1016\/j.media.2016.03.002","volume":"32","author":"B Irving","year":"2016","unstructured":"Irving B et al (2016) Pieces-of-parts for supervoxel segmentation with global context: application to DCE-MRI tumour delineation. Med Image Anal 32:69\u201383","journal-title":"Med Image Anal"},{"key":"2367_CR45","unstructured":"Kumar PMS, Chatterjee S (2016) \u201cComputer aided diagnostic for cancer detection using MRI images of brain"},{"key":"2367_CR46","doi-asserted-by":"crossref","unstructured":"Telrandhe SR, Pimpalkar A, Kendhe A (2016) \u201cDetection of brain tumor from MRI images by using segmentation & SVM,\u201d IEEE WCTFTR 2016 - Proc. 2016 World Conf. Futur. Trends Res. Innov. Soc. Welf","DOI":"10.1109\/STARTUP.2016.7583949"},{"key":"2367_CR47","doi-asserted-by":"crossref","unstructured":"Rao CH, Naganjaneyulu PV, Prasad KS (2017) \u201cBrain tumor detection and segmentation using conditional random field,\u201d Proc. - 7th IEEE Int. Adv. Comput. Conf. IACC 2017, pp. 807\u2013810","DOI":"10.1109\/IACC.2017.0166"},{"key":"2367_CR48","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1016\/j.media.2017.10.002","volume":"43","author":"X Zhao","year":"2018","unstructured":"Zhao X, Wu Y, Song G, Li Z, Zhang Y, Fan Y (2018) A deep learning model integrating FCNNs and CRFs for brain tumor segmentation. Med Image Anal 43:98\u2013111","journal-title":"Med Image Anal"},{"key":"2367_CR49","doi-asserted-by":"crossref","unstructured":"Srinivas B, Rao GS (2018) \u201cUnsupervised learning algorithms for MRI brain tumor segmentation,\u201d 2018 Conf. Signal Process. Commun. Eng. Syst. SPACES 2018, vol. 2018-Janua, pp 181\u2013184","DOI":"10.1109\/SPACES.2018.8316341"},{"key":"2367_CR50","unstructured":"Ajala Funmilola A, Oke OA, Adedeji TO, Alade OM, Adewusi EA (2012) \u201cFuzzy k-c-means clustering algorithm for medical image segmentation,\u201d vol. 2, no. 6, pp 21\u201333"},{"key":"2367_CR51","unstructured":"MT Scholar, C. Science, C. Science, and C. Science (2015) \u201cThe K-means clustering based Fuzzy edge detection technique on MRI images"},{"key":"2367_CR52","doi-asserted-by":"publisher","unstructured":"Abdel-maksoud E, Elmogy M, Al-awadi R (2015) \u201cBrain tumor segmentation based on a hybrid clustering technique,\u201d Egypt\u00a0Inform J\u00a016(1):71\u201381. https:\/\/doi.org\/10.1016\/j.eij.2015.01.003","DOI":"10.1016\/j.eij.2015.01.003"},{"key":"2367_CR53","doi-asserted-by":"publisher","unstructured":"Lang LF, Neumayer S, \u00d6ktem O, Sch\u00f6nlieb C (2019) \u201cTemplate-based image reconstruction from sparse tomographic data,\u201d Appl Math Optim\u00a082:1081\u20131109. https:\/\/doi.org\/10.1007\/s00245-019-09573-2","DOI":"10.1007\/s00245-019-09573-2"}],"container-title":["Medical & Biological Engineering & Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-021-02367-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-021-02367-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-021-02367-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,4]],"date-time":"2023-01-04T19:09:30Z","timestamp":1672859370000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-021-02367-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,17]]},"references-count":53,"journal-issue":{"issue":"7-8","published-print":{"date-parts":[[2021,8]]}},"alternative-id":["2367"],"URL":"https:\/\/doi.org\/10.1007\/s11517-021-02367-1","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"value":"0140-0118","type":"print"},{"value":"1741-0444","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,17]]},"assertion":[{"value":"27 February 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 April 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 July 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}