{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T22:20:08Z","timestamp":1775254808911,"version":"3.50.1"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"15","license":[{"start":{"date-parts":[[2020,4,24]],"date-time":"2020-04-24T00:00:00Z","timestamp":1587686400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,4,24]],"date-time":"2020-04-24T00:00:00Z","timestamp":1587686400000},"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":["Neural Comput & Applic"],"published-print":{"date-parts":[[2022,8]]},"DOI":"10.1007\/s00521-020-04935-2","type":"journal-article","created":{"date-parts":[[2020,4,24]],"date-time":"2020-04-24T06:25:52Z","timestamp":1587709552000},"page":"13133-13145","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Prediction and diagnosis of vertebral tumors on the Internet of Medical Things Platform using geometric rough propagation neural network"],"prefix":"10.1007","volume":"34","author":[{"given":"H.","family":"Fouad","sequence":"first","affiliation":[]},{"given":"Ahmed M.","family":"Soliman","sequence":"additional","affiliation":[]},{"given":"Azza S.","family":"Hassanein","sequence":"additional","affiliation":[]},{"given":"Haytham","family":"Al-Feel","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,4,24]]},"reference":[{"issue":"3","key":"4935_CR1","doi-asserted-by":"publisher","first-page":"567","DOI":"10.1016\/j.media.2014.02.002","volume":"18","author":"SS Chandra","year":"2014","unstructured":"Chandra SS, Xia Y, Engstrom C, Crozier S, Schwarz R, Fripp J (2014) Focused shape models for hip joint segmentation in 3D magnetic resonance images. Med Image Anal 18(3):567\u2013578","journal-title":"Med Image Anal"},{"key":"4935_CR2","unstructured":"Perea\u00f1ez M (2017) Enlargement, subdivision and individualization of statistical shape models: application to 3D medical image segmentation\u00a0(Doctoral dissertation, UniversitatPompeuFabra)"},{"key":"4935_CR3","doi-asserted-by":"publisher","first-page":"101638","DOI":"10.1016\/j.compmedimag.2019.05.006","volume":"77","author":"J Ma","year":"2019","unstructured":"Ma J, Wang A, Lin F, Wesarg S, Erdt M (2019) A novel robust kernel principal component analysis for nonlinear statistical shape modeling from erroneous data. Comput Med Imaging Gr 77:101638","journal-title":"Comput Med Imaging Gr"},{"issue":"6_Supple_B","key":"4935_CR4","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1302\/0301-620X.101B6.BJJ-2018-1073.R1","volume":"101","author":"N Liu","year":"2019","unstructured":"Liu N, Goodman SB, Lachiewicz PF, Wood KB (2019) Hip or spine surgery first? A survey of treatment order for patients with concurrent degenerative hip and spinal disorders. Bone Joint J 101(6_Supple_B):37\u201344","journal-title":"Bone Joint J"},{"issue":"3","key":"4935_CR5","doi-asserted-by":"publisher","first-page":"613","DOI":"10.1016\/j.cmpb.2013.05.017","volume":"111","author":"YC Lu","year":"2013","unstructured":"Lu YC, Untaroiu CD (2013) Statistical shape analysis of clavicular cortical bone with applications to the development of mean and boundary shape models. Comput Methods Programs Biomed 111(3):613\u2013628","journal-title":"Comput Methods Programs Biomed"},{"key":"4935_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2019.05.027","author":"PM Shakeel","year":"2019","unstructured":"Shakeel PM, Burhanuddin MA, Desa MI (2019) Lung cancer detection from CT image using improved profuse clustering and deep learning instantaneously trained neural networks. Measurement. https:\/\/doi.org\/10.1016\/j.measurement.2019.05.027","journal-title":"Measurement"},{"issue":"1","key":"4935_CR7","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1186\/s12938-017-0340-0","volume":"16","author":"J Wang","year":"2017","unstructured":"Wang J, Shi C (2017) Automatic construction of statistical shape models using deformable simplex meshes with vector field convolution energy. Biomed Eng Online 16(1):49","journal-title":"Biomed Eng Online"},{"issue":"7","key":"4935_CR8","doi-asserted-by":"publisher","first-page":"1227","DOI":"10.1109\/TMI.2013.2244903","volume":"32","author":"S Kadoury","year":"2013","unstructured":"Kadoury S, Labelle H, Paragios N (2013) Spine segmentation in medical images using manifold embeddings and higher-order MRFs. IEEE Trans Med Imaging 32(7):1227\u20131238","journal-title":"IEEE Trans Med Imaging"},{"key":"4935_CR9","doi-asserted-by":"publisher","DOI":"10.1109\/access.2018.2883957","author":"PM Shakeel","year":"2019","unstructured":"Shakeel PM, Tobely TE, Al-Feel H, Manogaran G, Baskar S (2019) Neural network based brain tumor detection using wireless infrared imaging sensor. IEEE Access. https:\/\/doi.org\/10.1109\/access.2018.2883957","journal-title":"IEEE Access"},{"key":"4935_CR10","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1016\/j.compbiomed.2016.03.009","volume":"72","author":"JS Athertya","year":"2016","unstructured":"Athertya JS, Kumar GS (2016) Automatic segmentation of vertebral contours from CT images using fuzzy corners. Comput Biol Med 72:75\u201389","journal-title":"Comput Biol Med"},{"issue":"1","key":"4935_CR11","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1186\/s13047-016-0178-x","volume":"10","author":"AU Melinska","year":"2017","unstructured":"Melinska AU, Romaszkiewicz P, Wagel J, Antosik B, Sasiadek M, Iskander DR (2017) Statistical shape models of cuboid, navicular and talus bones. J Foot Ankle Res 10(1):6","journal-title":"J Foot Ankle Res"},{"key":"4935_CR12","volume-title":"Recent advances in computational methods and clinical applications for spine imaging","year":"2015","unstructured":"Yao J, Glocker B, Klinder T, Li S (eds) (2015) Recent advances in computational methods and clinical applications for spine imaging. Springer, Cham"},{"key":"4935_CR13","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1007\/978-3-319-14148-0_13","volume-title":"Recent advances in computational methods and clinical applications for spine imaging","author":"R Haq","year":"2015","unstructured":"Haq R, Aras R, Besachio DA, Borgie RC, Audette MA (2015) Minimally supervised segmentation and meshing of 3D intervertebral discs of the lumbar spine for discectomy simulation. In: Yao J, Glocker B, Klinder T, Li S (eds) Recent advances in computational methods and clinical applications for spine imaging. Springer, Cham, pp 143\u2013155"},{"issue":"138","key":"4935_CR14","doi-asserted-by":"publisher","first-page":"20170829","DOI":"10.1098\/rsif.2017.0829","volume":"15","author":"CE Lavecchia","year":"2018","unstructured":"Lavecchia CE, Espino DM, Moerman KM, Tse KM, Robinson D, Lee PVS, Shepherd DET (2018) Lumbar model generator: a tool for the automated generation of a parametric scalable model of the lumbar spine. J R Soc Interface 15(138):20170829","journal-title":"J R Soc Interface"},{"issue":"3","key":"4935_CR15","doi-asserted-by":"publisher","first-page":"578","DOI":"10.1007\/s10278-012-9552-9","volume":"26","author":"Q Zheng","year":"2013","unstructured":"Zheng Q, Lu Z, Feng Q, Ma J, Yang W, Chen C, Chen W (2013) Adaptive segmentation of vertebral bodies from sagittal MR images based on local spatial information and Gaussian weighted Chi square distance. J Digit Imaging 26(3):578\u2013593","journal-title":"J Digit Imaging"},{"key":"4935_CR16","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1016\/j.compbiomed.2014.10.003","volume":"55","author":"S Yamaguchi","year":"2014","unstructured":"Yamaguchi S, Satake K, Yamaji Y, Chen YW, Tanaka HT (2014) Three-dimensional semiautomatic liver segmentation method for non-contrast computed tomography based on a correlation map of the locoregional histogram and probabilistic atlas. Comput Biol Med 55:79\u201385","journal-title":"Comput Biol Med"},{"key":"4935_CR17","doi-asserted-by":"crossref","unstructured":"Elnakib A, Gimel\u2019farb G, Suri JS, El-Baz A (2011) Medical image segmentation: a brief survey. In: Multi modality state-of-the-art medical image segmentation and registration methodologies. Springer, New York, pp 1\u201339","DOI":"10.1007\/978-1-4419-8204-9_1"},{"issue":"8Part1","key":"4935_CR18","doi-asserted-by":"publisher","first-page":"4700","DOI":"10.1118\/1.4955118","volume":"43","author":"H Li","year":"2016","unstructured":"Li H, Chen HC, Dolly S, Li H, Fischer-Valuck B, Victoria J et al (2016) An integrated model-driven method for in treatment upper airway motion tracking using cine MRI in head and neck radiation therapy. Med Phys 43(8Part1):4700\u20134710","journal-title":"Med Phys"},{"key":"4935_CR19","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-018-1058-y","author":"KP Sridhar","year":"2018","unstructured":"Sridhar KP, Baskar S, Shakeel PM, Dhulipala VS (2018) Developing brain abnormality recognize system using multi-objective pattern producing neural network. J Ambient Intell Hum Comput. https:\/\/doi.org\/10.1007\/s12652-018-1058-y","journal-title":"J Ambient Intell Hum Comput"},{"issue":"2","key":"4935_CR20","doi-asserted-by":"publisher","first-page":"363","DOI":"10.1587\/transinf.E94.D.363","volume":"94","author":"S Lee","year":"2011","unstructured":"Lee S, Cho S, Ro YM (2011) Enhanced distal radius segmentation in DXA using modified ASM. IEICE TRANSACTIONS on Information and Systems 94(2):363\u2013370","journal-title":"IEICE TRANSACTIONS on Information and Systems"},{"key":"4935_CR21","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1016\/j.media.2017.11.012","volume":"44","author":"N Ravikumar","year":"2018","unstructured":"Ravikumar N, Gooya A, \u00c7imen S, Frangi AF, Taylor ZA (2018) Group-wise similarity registration of point sets using Student\u2019s t-mixture model for statistical shape models. Med Image Anal 44:156\u2013176","journal-title":"Med Image Anal"},{"key":"4935_CR22","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1016\/j.bone.2013.12.006","volume":"60","author":"N Sarkalkan","year":"2014","unstructured":"Sarkalkan N, Weinans H, Zadpoor AA (2014) Statistical shape and appearance models of bones. Bone 60:129\u2013140","journal-title":"Bone"},{"issue":"2","key":"4935_CR23","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1006\/cviu.1999.0814","volume":"77","author":"C Lorenz","year":"2000","unstructured":"Lorenz C, Krahnst\u00f6ver N (2000) Generation of point-based 3D statistical shape models for anatomical objects. Comput Vis Image Underst 77(2):175\u2013191","journal-title":"Comput Vis Image Underst"},{"issue":"1","key":"4935_CR24","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1007\/s11548-014-1094-9","volume":"10","author":"R Haq","year":"2015","unstructured":"Haq R, Aras R, Besachio DA, Borgie RC, Audette MA (2015) 3D lumbar spine intervertebral disc segmentation and compression simulation from MRI using shape-aware models. Int J Comput Assist Radiol Surg 10(1):45\u201354","journal-title":"Int J Comput Assist Radiol Surg"},{"issue":"10","key":"4935_CR25","doi-asserted-by":"publisher","first-page":"1890","DOI":"10.1109\/TMI.2013.2268424","volume":"32","author":"A Rasoulian","year":"2013","unstructured":"Rasoulian A, Rohling R, Abolmaesumi P (2013) Lumbar spine segmentation using a statistical multi-vertebrae anatomical shape\u2009+\u2009pose model. IEEE Trans Med Imaging 32(10):1890\u20131900","journal-title":"IEEE Trans Med Imaging"},{"key":"4935_CR26","unstructured":"http:\/\/spineweb.digitalimaginggroup.ca\/spineweb\/index.php?n=Main.Datasets"},{"issue":"3","key":"4935_CR27","doi-asserted-by":"publisher","first-page":"338","DOI":"10.1016\/j.jocd.2019.02.001","volume":"22","author":"WD Leslie","year":"2019","unstructured":"Leslie WD, Luo Y, Yang S, Goertzen AL, Ahmed S, Delubac I, Lix LM (2019) Fracture risk indices from DXA-based finite element analysis predict incident fractures independently from FRAX: the Manitoba BMD Registry. J Clin Densitom 22(3):338\u2013345","journal-title":"J Clin Densitom"},{"issue":"4","key":"4935_CR28","doi-asserted-by":"publisher","first-page":"669","DOI":"10.14245\/ns.1938402.201","volume":"16","author":"E Massaad","year":"2019","unstructured":"Massaad E, Fatima N, Hadzipasic M, Alvarez-Breckenridge C, Shankar GM, Shin JH (2019) Predictive analytics in spine oncology research: first steps, limitations, and future directions. Neurospine 16(4):669","journal-title":"Neurospine"},{"key":"4935_CR29","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1016\/j.ejrad.2018.11.011","volume":"110","author":"A Urbaneja","year":"2019","unstructured":"Urbaneja A, De Verbizier J, Formery AS, Tobon-Gomez C, Nace L, Blum A, Teixeira PAG (2019) Automatic rib cage unfolding with CT cylindrical projection reformat in polytraumatized patients for rib fracture detection and characterization: feasibility and clinical application. Eur J Radiol 110:121\u2013127","journal-title":"Eur J Radiol"},{"issue":"1","key":"4935_CR30","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1007\/s10439-019-02363-5","volume":"48","author":"N Yoganandan","year":"2020","unstructured":"Yoganandan N, DeVogel N, Moore J, Pintar F, Banerjee A, Zhang J (2020) Human lumbar spine responses from vertical loading: ranking of forces via Brier score metrics and injury risk curves. Ann Biomed Eng 48(1):79\u201391","journal-title":"Ann Biomed Eng"},{"issue":"3\u20134","key":"4935_CR31","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1016\/j.rse.2004.02.015","volume":"91","author":"L Boschetti","year":"2004","unstructured":"Boschetti L, Flasse SP, Brivio PA (2004) Analysis of the conflict between omission and commission in low spatial resolution dichotomic thematic products: the Pareto Boundary. Remote Sens Environ 91(3\u20134):280\u2013292","journal-title":"Remote Sens Environ"},{"issue":"6","key":"4935_CR32","doi-asserted-by":"publisher","first-page":"1341","DOI":"10.2214\/AJR.19.21505","volume":"213","author":"CWC Huang","year":"2019","unstructured":"Huang CWC, Ali A, Chang YM, Bezuidenhout AF, Ivanovic V, Rojas R, Bhadelia RA (2019) Performance of on-call radiology residents in interpreting total spine mri studies for the detection of spinal cord compression or cauda equina compression. Am J Roentgenol 213(6):1341\u20131347","journal-title":"Am J Roentgenol"},{"issue":"3","key":"4935_CR33","doi-asserted-by":"publisher","first-page":"282","DOI":"10.1177\/1533317515603957","volume":"31","author":"M Dessouky","year":"2015","unstructured":"Dessouky M, Elrashidy M, Taha T, Abdelkader H (2015) Computer-aided diagnosis system for Alzheimer\u2019s disease using different discrete transform techniques. Am J Alzheimer\u2019s Dis Other Dementiasr 31(3):282\u2013293","journal-title":"Am J Alzheimer\u2019s Dis Other Dementiasr"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-04935-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-020-04935-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-04935-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,23]],"date-time":"2022-07-23T10:06:07Z","timestamp":1658570767000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-020-04935-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,4,24]]},"references-count":33,"journal-issue":{"issue":"15","published-print":{"date-parts":[[2022,8]]}},"alternative-id":["4935"],"URL":"https:\/\/doi.org\/10.1007\/s00521-020-04935-2","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,4,24]]},"assertion":[{"value":"2 February 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 April 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 April 2020","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"}}]}}