{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T16:24:30Z","timestamp":1784823870652,"version":"3.55.0"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"13","license":[{"start":{"date-parts":[[2022,3,12]],"date-time":"2022-03-12T00:00:00Z","timestamp":1647043200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,3,12]],"date-time":"2022-03-12T00:00:00Z","timestamp":1647043200000},"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":["Appl Intell"],"published-print":{"date-parts":[[2022,10]]},"DOI":"10.1007\/s10489-022-03184-1","type":"journal-article","created":{"date-parts":[[2022,3,12]],"date-time":"2022-03-12T07:03:01Z","timestamp":1647068581000},"page":"15237-15258","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Low-contrast lesion segmentation in advanced MRI experiments by time-domain Ricker-type wavelets and fuzzy 2-means"],"prefix":"10.1007","volume":"52","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7651-345X","authenticated-orcid":false,"given":"Orcan","family":"Alpar","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rafael","family":"Dolezal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pavel","family":"Ryska","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ondrej","family":"Krejcar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,3,12]]},"reference":[{"key":"3184_CR1","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1016\/j.eswa.2018.11.017","volume":"120","author":"O Alpar","year":"2019","unstructured":"Alpar O (2019) A novel fuzzy curvature method for recognition of anterior forearm subcutaneous veins by thermal imaging. Expert Syst Appl 120:33\u201342","journal-title":"Expert Syst Appl"},{"key":"3184_CR2","doi-asserted-by":"publisher","first-page":"102704","DOI":"10.1016\/j.jtherbio.2020.102704","volume":"93","author":"O Alpar","year":"2020","unstructured":"Alpar O (2020) Nakagami imaging with related distributions for advanced thermogram pseudocolorization. J Therm Biol 93:102704","journal-title":"J Therm Biol"},{"key":"3184_CR3","doi-asserted-by":"crossref","unstructured":"Alpar, O., & Krejcar, O. (2018a). A comparative study on chrominance based methods in dorsal hand recognition: single image case. International conference on industrial, engineering and other applications of applied intelligent systems (pp. 711\u2013721). Springer, Cham","DOI":"10.1007\/978-3-319-92058-0_68"},{"key":"3184_CR4","doi-asserted-by":"crossref","unstructured":"Alpar, O., & Krejcar, O. (2018b). Detection of irregular thermoregulation in hand thermography by fuzzy C-means. In international conference on bioinformatics and biomedical engineering (pp. 255\u2013265). Springer, Cham","DOI":"10.1007\/978-3-319-78759-6_24"},{"key":"3184_CR5","doi-asserted-by":"publisher","first-page":"107481","DOI":"10.1016\/j.asoc.2021.107481","volume":"108","author":"O Alpar","year":"2021","unstructured":"Alpar O, Krejcar O, Dolezal R (2021) Distribution-based imaging for multiple sclerosis lesion segmentation using specialized fuzzy 2-means powered by Nakagami transmutations. Appl Soft Comput 108:107481","journal-title":"Appl Soft Comput"},{"issue":"20","key":"3184_CR6","doi-asserted-by":"publisher","first-page":"15965","DOI":"10.1007\/s00521-019-04650-7","volume":"32","author":"J Amin","year":"2020","unstructured":"Amin J, Sharif M, Raza M, Saba T, Sial R, Shad SA (2020) Brain tumor detection: a long short-term memory (LSTM)-based learning model. Neural Comput & Applic 32(20):15965\u201315973","journal-title":"Neural Comput & Applic"},{"key":"3184_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.neuroimage.2019.03.068","volume":"196","author":"S Aslani","year":"2019","unstructured":"Aslani S, Dayan M, Storelli L, Filippi M, Murino V, Rocca MA, Sona D (2019) Multi-branch convolutional neural network for multiple sclerosis lesion segmentation. NeuroImage 196:1\u201315","journal-title":"NeuroImage"},{"issue":"1","key":"3184_CR8","doi-asserted-by":"publisher","first-page":"671","DOI":"10.1007\/s11063-020-10398-2","volume":"53","author":"W Ayadi","year":"2021","unstructured":"Ayadi W, Elhamzi W, Charfi I, Atri M (2021) Deep CNN for brain tumor classification. Neural Process Lett 53(1):671\u2013700","journal-title":"Neural Process Lett"},{"key":"3184_CR9","doi-asserted-by":"crossref","unstructured":"Billast M, Meyer MI, Sima DM, Robben D (2019) Improved inter-scanner MS lesion segmentation by adversarial training on longitudinal data, International MICCAI Brainlesion workshop (pp. 98\u2013107). Springer, Cham","DOI":"10.1007\/978-3-030-46640-4_10"},{"key":"3184_CR10","doi-asserted-by":"publisher","first-page":"107784","DOI":"10.1016\/j.patcog.2020.107784","volume":"112","author":"A Bose","year":"2021","unstructured":"Bose A, Mali K (2021) Type-reduced vague possibilistic fuzzy clustering for medical images. Pattern Recogn 112:107784","journal-title":"Pattern Recogn"},{"issue":"3","key":"3184_CR11","doi-asserted-by":"publisher","first-page":"674","DOI":"10.1002\/ima.22407","volume":"30","author":"PL Chithra","year":"2020","unstructured":"Chithra PL, Dheepa G (2020) Di-phase midway convolution and deconvolution network for brain tumor segmentation in MRI images. Int J Imaging Syst Technol 30(3):674\u2013686","journal-title":"Int J Imaging Syst Technol"},{"key":"3184_CR12","doi-asserted-by":"publisher","first-page":"113200","DOI":"10.1016\/j.eswa.2020.113200","volume":"147","author":"L Corbat","year":"2020","unstructured":"Corbat L, Nauval M, Henriet J, Lapayre JC (2020) A fusion method based on deep learning and case-based reasoning which improves the resulting medical image segmentations. Ions Expert Systems with Appl 147:113200","journal-title":"Ions Expert Systems with Appl"},{"key":"3184_CR13","unstructured":"Cordier N, Menze B, Delingette H, Ayache N (2013) Patch-based segmentation of brain tissues. MICCAI challenge on multimodal brain tumor segmentation (pp. 6-17). IEEE."},{"key":"3184_CR14","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1016\/j.compbiomed.2016.11.005","volume":"80","author":"WG Flores","year":"2017","unstructured":"Flores WG, de Albuquerque Pereira WC (2017) A contrast enhancement method for improving the segmentation of breast lesions on ultrasonography. Comput Biol Med 80:14\u201323","journal-title":"Comput Biol Med"},{"issue":"3","key":"3184_CR15","doi-asserted-by":"publisher","first-page":"612","DOI":"10.1002\/mrm.20198","volume":"52","author":"EM Haacke","year":"2004","unstructured":"Haacke EM, Xu Y, Cheng YC, Reichenbach JR (2004) Susceptibility weighted imaging (SWI). Magnetic Resonance Med: Off JInt Soc Magnetic Resonance Med 52(3):612\u2013618","journal-title":"Magnetic Resonance Med: Off JInt Soc Magnetic Resonance Med"},{"key":"3184_CR16","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, Davy A, Warde-Farley D, Biard A, Courville A, Bengio Y, Larochelle H (2017) Brain tumor segmentation with deep neural networks. Med Image Anal 35:18\u201331","journal-title":"Med Image Anal"},{"key":"3184_CR17","doi-asserted-by":"publisher","first-page":"105809","DOI":"10.1016\/j.cmpb.2020.105809","volume":"198","author":"A Khosravanian","year":"2021","unstructured":"Khosravanian A, Rahmanimanesh M, Keshavarzi P, Mozaffari S (2021) Fast level set method for glioma brain tumor segmentation based on Superpixel fuzzy clustering and lattice Boltzmann method. Comput Methods Prog Biomed 198:105809","journal-title":"Comput Methods Prog Biomed"},{"key":"3184_CR18","doi-asserted-by":"publisher","unstructured":"Khouloud S, Ahlem M, Fadel T (2021) W-net and inception residual network for skin lesion segmentation and classification. Appl Intell. https:\/\/doi.org\/10.1007\/s10489-021-02652-4","DOI":"10.1007\/s10489-021-02652-4"},{"issue":"11","key":"3184_CR19","doi-asserted-by":"publisher","DOI":"10.2196\/jmir.2930","volume":"15","author":"M Kistler","year":"2013","unstructured":"Kistler M, Bonaretti S, Pfahrer M, Niklaus R, B\u00fcchler P (2013) The virtual skeleton database: an open access repository for biomedical research and collaboration. J Med Internet Res 15(11):e245","journal-title":"J Med Internet Res"},{"key":"3184_CR20","doi-asserted-by":"publisher","first-page":"113159","DOI":"10.1016\/j.eswa.2019.113159","volume":"146","author":"A Kouhi","year":"2020","unstructured":"Kouhi A, Seyedarabi H, Aghagolzadeh A (2020) Robust FCM clustering algorithm with combined spatial constraint and membership matrix local information for brain MRI segmentation. Expert Syst Appl 146:113159","journal-title":"Expert Syst Appl"},{"issue":"2","key":"3184_CR21","doi-asserted-by":"publisher","first-page":"322","DOI":"10.1007\/s10278-018-0149-9","volume":"32","author":"SN Kumar","year":"2019","unstructured":"Kumar SN, Fred AL, Varghese PS (2019) Suspicious lesion segmentation on brain, mammograms and breast MR images using new optimized spatial feature based super-pixel fuzzy c-means clustering. J Digit Imaging 32(2):322\u2013335","journal-title":"J Digit Imaging"},{"key":"3184_CR22","doi-asserted-by":"crossref","unstructured":"Kumar V, Webb JM, Gregory A, Denis M, Meixner DD, Bayat M, Alizad A (2018) Automated and real-time segmentation of suspicious breast masses using convolutional neural network. PLoS One 13(5):e0195816","DOI":"10.1371\/journal.pone.0195816"},{"key":"3184_CR23","doi-asserted-by":"publisher","first-page":"114262","DOI":"10.1016\/j.eswa.2020.114262","volume":"168","author":"X Lei","year":"2021","unstructured":"Lei X, Yu X, Chi J, Wang Y, Zhang J, Wu C (2021) Brain tumor segmentation in MR images using a sparse constrained level set algorithm. Expert Syst Appl 168:114262","journal-title":"Expert Syst Appl"},{"key":"3184_CR24","doi-asserted-by":"publisher","first-page":"105279","DOI":"10.1016\/j.knosys.2019.105279","volume":"191","author":"F Li","year":"2020","unstructured":"Li F, Shang C, Li Y, Shen Q (2020) Interpretable mammographic mass classification with fuzzy interpolative reasoning. Knowl-Based Syst 191:105279","journal-title":"Knowl-Based Syst"},{"key":"3184_CR25","unstructured":"Meier R, Bauer S, Slotboom J, Wiest R, Reyes M (2013) A hybrid model for multimodal brain tumor segmentation. Multimodal Brain Tumor Segm, (pp. 31-37.)."},{"issue":"10","key":"3184_CR26","doi-asserted-by":"publisher","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","volume":"34","author":"BH Menze","year":"2014","unstructured":"Menze BH, Jakab A, Bauer S, Kalpathy-Cramer J, Farahani K, Kirby J et al (2014) The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans Med Imaging 34(10):1993\u20132024","journal-title":"IEEE Trans Med Imaging"},{"key":"3184_CR27","doi-asserted-by":"publisher","first-page":"101557","DOI":"10.1016\/j.media.2019.101557","volume":"59","author":"T Nair","year":"2020","unstructured":"Nair T, Precup D, Arnold DL, Arbel T (2020) Exploring uncertainty measures in deep networks for multiple sclerosis lesion detection and segmentation. Med Image Anal 59:101557","journal-title":"Med Image Anal"},{"issue":"2","key":"3184_CR28","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1007\/s10916-018-1135-y","volume":"43","author":"A Natarajan","year":"2019","unstructured":"Natarajan A, Kumarasamy S (2019) Efficient segmentation of brain tumor using FL-SNM with a metaheuristic approach to optimization. J Med Syst 43(2):25\u201339","journal-title":"J Med Syst"},{"key":"3184_CR29","doi-asserted-by":"crossref","unstructured":"Nie D, Shen D (2020) Adversarial confidence learning for medical image segmentation and synthesis. Int J Comput Vision, 1-20.","DOI":"10.1007\/s11263-020-01321-2"},{"key":"3184_CR30","doi-asserted-by":"publisher","first-page":"101608","DOI":"10.1016\/j.nicl.2018.11.018","volume":"21","author":"SJ Peng","year":"2019","unstructured":"Peng SJ, Lee CC, Wu HM, Lin CJ, Shiau CY, Guo WY, Yang HC (2019) Fully automated tissue segmentation of the prescription isodose region delineated through the gamma knife plan for cerebral arteriovenous malformation (AVM) using fuzzy C-means (FCM) clustering. NeuroImage: Clin 21:101608","journal-title":"NeuroImage: Clin"},{"issue":"2","key":"3184_CR31","doi-asserted-by":"publisher","first-page":"572","DOI":"10.1109\/TMI.2018.2867620","volume":"38","author":"S Pramanik","year":"2018","unstructured":"Pramanik S, Banik D, Bhattacharjee D, Nasipuri M, Bhowmik MK, Majumdar G (2018) Suspicious-region segmentation from breast Thermogram using DLPE-based level set method. IEEE Trans Med Imaging 38(2):572\u2013584","journal-title":"IEEE Trans Med Imaging"},{"key":"3184_CR32","doi-asserted-by":"publisher","first-page":"598","DOI":"10.1016\/j.eswa.2018.10.040","volume":"118","author":"ZU Rehman","year":"2019","unstructured":"Rehman ZU, Naqvi SS, Khan TM, Khan MA, Bashir T (2019) Fully automated multi-parametric brain tumour segmentation using superpixel based classification. Expert Syst Appl 118:598\u2013613","journal-title":"Expert Syst Appl"},{"key":"3184_CR33","doi-asserted-by":"crossref","unstructured":"Reza S, Iftekharuddin KM (2013) Multi-class abnormal brain tissue segmentation using texture. Multimodal Brain Tumor Segment, (pp. 38-42).","DOI":"10.1364\/QMI.2013.QW2G.2"},{"key":"3184_CR34","doi-asserted-by":"crossref","unstructured":"Shivhare SN, Kumar N (2020) Brain tumor detection using manifold ranking in flair mri. Proceedings of ICETIT 2019. Springer, Cham,\u00a0pp 292\u2013305","DOI":"10.1007\/978-3-030-30577-2_25"},{"key":"3184_CR35","doi-asserted-by":"publisher","first-page":"112855","DOI":"10.1016\/j.eswa.2019.112855","volume":"139","author":"VK Singh","year":"2020","unstructured":"Singh VK, Rashwan HA, Romani S, Akram F, Pandey N, Sarker MM, Torrents-Barrena J (2020) Breast tumor segmentation and shape classification in mammograms using generative adversarial and convolutional neural network. Expert Syst Appl 139:112855","journal-title":"Expert Syst Appl"},{"issue":"2","key":"3184_CR36","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1007\/s11548-016-1483-3","volume":"12","author":"M Soltaninejad","year":"2017","unstructured":"Soltaninejad M, Yang G, Lambrou T, Allinson N, Jones TL, Barrick TR, Ye X (2017) Automated brain tumour detection and segmentation using superpixel-based extremely randomized trees in FLAIR MRI. Int J Comput Assist Radiol Surg 12(2):183\u2013203","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"3184_CR37","doi-asserted-by":"publisher","first-page":"113131","DOI":"10.1016\/j.eswa.2019.113131","volume":"145","author":"LI Song","year":"2020","unstructured":"Song LI, Geoffrey KF, Kaijian HE (2020) Bottleneck feature supervised U-net for pixel-wise liver and tumor segmentation. Expert Syst Appl 145:113131","journal-title":"Expert Syst Appl"},{"key":"3184_CR38","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1016\/j.neucom.2020.10.031","volume":"423","author":"J Sun","year":"2021","unstructured":"Sun J, Peng Y, Guo Y, Li D (2021) Segmentation of the multimodal brain tumor image used the multi-pathway architecture method based on 3D FCN. Neurocomputing 423:34\u201345","journal-title":"Neurocomputing"},{"key":"3184_CR39","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1016\/j.knosys.2018.05.042","volume":"158","author":"TY Tan","year":"2018","unstructured":"Tan TY, Zhang L, Neoh SC, Lim CP (2018) Intelligent skin cancer detection using enhanced particle swarm optimization. Knowl-Based Syst 158:118\u2013135","journal-title":"Knowl-Based Syst"},{"key":"3184_CR40","doi-asserted-by":"publisher","first-page":"104807","DOI":"10.1016\/j.knosys.2019.06.015","volume":"187","author":"TY Tan","year":"2020","unstructured":"Tan TY, Zhang L, Lim CP (2020) Adaptive melanoma diagnosis using evolving clustering, ensemble and deep neural networks. Knowl-Based Syst 187:104807","journal-title":"Knowl-Based Syst"},{"key":"3184_CR41","unstructured":"Tustison N, Wintermark M, Durst C, Avants B (2013) Ants andarboles. Multimodal Brain Tumor Segment, pp 47\u201350"},{"issue":"3","key":"3184_CR42","doi-asserted-by":"publisher","first-page":"871","DOI":"10.1007\/s10044-017-0597-8","volume":"20","author":"K Usman","year":"2017","unstructured":"Usman K, Rajpoot K (2017) Brain tumor classification from multi-modality MRI using wavelets and machine learning. Pattern Anal Applic 20(3):871\u2013881","journal-title":"Pattern Anal Applic"},{"issue":"7","key":"3184_CR43","doi-asserted-by":"publisher","first-page":"1559","DOI":"10.1109\/TPAMI.2018.2840695","volume":"41","author":"G Wang","year":"2018","unstructured":"Wang G, Zuluaga MA, Li W, Pratt R, Patel PA, Aertsen M, Vercauteren T (2018) DeepIGeoS: a deep interactive geodesic framework for medical image segmentation. IEEE Trans Patt Anal Mach Intell 41(7):1559\u20131572","journal-title":"IEEE Trans Patt Anal Mach Intell"},{"key":"3184_CR44","doi-asserted-by":"publisher","unstructured":"Wang H, Hu J, Song Y, Zhang L, Bai S, Yi Z (2021) Multi-view fusion segmentation for brain glioma on CT images. Appl Intell. https:\/\/doi.org\/10.1007\/s10489-021-02784-7","DOI":"10.1007\/s10489-021-02784-7"},{"key":"3184_CR45","doi-asserted-by":"publisher","first-page":"695","DOI":"10.1016\/j.future.2019.12.035","volume":"105","author":"J Wang","year":"2020","unstructured":"Wang J, Liu M, Zhang C, Xu H, Zhang L, Zhao Y (2020) An adaptive sparse Bayesian model combined with probabilistic label fusion for multiple sclerosis lesion segmentation in brain MRI. Futur Gener Comput Syst 105:695\u2013704","journal-title":"Futur Gener Comput Syst"},{"key":"3184_CR46","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.ins.2019.05.015","volume":"496","author":"W Wang","year":"2019","unstructured":"Wang W, Chen Z, Yuan X, Wu X (2019) Adaptive image enhancement method for correcting low-illumination images. Inf Sci 496:25\u201341","journal-title":"Inf Sci"},{"issue":"2","key":"3184_CR47","doi-asserted-by":"publisher","first-page":"621","DOI":"10.1109\/TCSVT.2020.2981530","volume":"31","author":"ZZ Wang","year":"2021","unstructured":"Wang ZZ (2021) Automatic localization and segmentation of the ventricle in magnetic resonance images. IEEE Trans Circuits Syst Video Technol 31(2):621\u2013631","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"3184_CR48","doi-asserted-by":"publisher","first-page":"988","DOI":"10.1016\/j.asoc.2017.05.025","volume":"70","author":"ZZ Wang","year":"2018","unstructured":"Wang ZZ, Yang YM (2018) A non-iterative clustering based soft segmentation approach for a class of fuzzy images. Appl Soft Comput 70:988\u2013999","journal-title":"Appl Soft Comput"},{"key":"3184_CR49","doi-asserted-by":"crossref","unstructured":"Zhang W, Yang G, Huang H, Yang W, Xu X, Liu Y, Lai X (2021) ME-net: multi-encoder net framework for brain tumor segmentation. Int J Imaging Syst Technol, 1-15.","DOI":"10.1002\/ima.22571"},{"key":"3184_CR50","doi-asserted-by":"publisher","first-page":"114566","DOI":"10.1016\/j.eswa.2021.114566","volume":"170","author":"X Zhou","year":"2021","unstructured":"Zhou X, Li X, Hu K, Zhang Y, Chen Z, Gao X (2021) ERV-net: an efficient 3D residual neural network for brain tumor segmentation. Expert Syst Appl 170:114566","journal-title":"Expert Syst Appl"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-03184-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-022-03184-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-03184-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T09:59:02Z","timestamp":1664618342000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-022-03184-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,12]]},"references-count":50,"journal-issue":{"issue":"13","published-print":{"date-parts":[[2022,10]]}},"alternative-id":["3184"],"URL":"https:\/\/doi.org\/10.1007\/s10489-022-03184-1","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,12]]},"assertion":[{"value":"6 January 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 March 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}