{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T13:16:31Z","timestamp":1740143791294,"version":"3.37.3"},"reference-count":122,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,11,16]],"date-time":"2022-11-16T00:00:00Z","timestamp":1668556800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,11,16]],"date-time":"2022-11-16T00:00:00Z","timestamp":1668556800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2023,1]]},"DOI":"10.1007\/s11517-022-02714-w","type":"journal-article","created":{"date-parts":[[2022,11,16]],"date-time":"2022-11-16T18:02:38Z","timestamp":1668621758000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["ConvNets for automatic detection of polyglutamine SCAs from brain MRIs: state of the art applications"],"prefix":"10.1007","volume":"61","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4719-8264","authenticated-orcid":false,"given":"Robin","family":"Cabeza-Ruiz","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1628-2703","authenticated-orcid":false,"given":"Luis","family":"Vel\u00e1zquez-P\u00e9rez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5741-5168","authenticated-orcid":false,"given":"Roberto","family":"P\u00e9rez-Rodr\u00edguez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9730-9228","authenticated-orcid":false,"given":"Kathrin","family":"Reetz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,11,16]]},"reference":[{"key":"2714_CR1","doi-asserted-by":"publisher","first-page":"1357","DOI":"10.1093\/brain\/awl081","volume":"129","author":"AM Due\u00f1as","year":"2006","unstructured":"Due\u00f1as AM, Goold R, Giunti P (2006) Molecular pathogenesis of spinocerebellar ataxias. Brain 129:1357\u20131370. https:\/\/doi.org\/10.1093\/brain\/awl081","journal-title":"Brain"},{"key":"2714_CR2","doi-asserted-by":"publisher","unstructured":"Stevanin G, Brice A (2008) Spinocerebellar ataxia 17 ( SCA17 ) and Huntington \u2019 s disease-like 4 ( HDL4 ). Cerebellum. 7. https:\/\/doi.org\/10.1007\/s12311-008-0016-1","DOI":"10.1007\/s12311-008-0016-1"},{"key":"2714_CR3","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1080\/14734220510007914","volume":"4","author":"M Manto","year":"2005","unstructured":"Manto M (2005) The wide spectrum of spinocerebellar ataxias ( SCAs ). The Cerebellum 4:2\u20136. https:\/\/doi.org\/10.1080\/14734220510007914","journal-title":"The Cerebellum"},{"key":"2714_CR4","doi-asserted-by":"publisher","first-page":"1133","DOI":"10.1590\/S0004-282X2009000600035","volume":"67","author":"HAG Teive","year":"2009","unstructured":"Teive HAG (2009) Spinocerebellar ataxias. Arq Neuropsiquiatr 67:1133\u20131142. https:\/\/doi.org\/10.1590\/S0004-282X2009000600035","journal-title":"Arq Neuropsiquiatr"},{"key":"2714_CR5","doi-asserted-by":"publisher","first-page":"613","DOI":"10.1038\/nrn.2017.92","volume":"18","author":"HL Paulson","year":"2017","unstructured":"Paulson HL, Shakkottai VG, Clark HB, Orr HT (2017) Polyglutamine spinocerebellar ataxias \u2014 from genes to potential treatments. Nat Publ Gr 18:613\u2013626. https:\/\/doi.org\/10.1038\/nrn.2017.92","journal-title":"Nat Publ Gr"},{"key":"2714_CR6","doi-asserted-by":"publisher","first-page":"1405","DOI":"10.3174\/ajnr.A4760","volume":"37","author":"A Klaes","year":"2016","unstructured":"Klaes A, Reckziegel E, Franca MC, Rezende TJ, Vedolin LM, Jardim LB, Saute JA (2016) MR Imaging in Spinocerebellar Ataxias\u202f: A Systematic Review. Am J f Neuroradiol 37:1405\u20131412","journal-title":"Am J f Neuroradiol"},{"key":"2714_CR7","doi-asserted-by":"publisher","first-page":"702","DOI":"10.1212\/WNL.58.5.702","volume":"58","author":"BPC Van De Warrenburg","year":"2002","unstructured":"Van De Warrenburg BPC, Sinke RJ, Bemelmans CCV, Scheffer H (2002) Spinocerebellar ataxias in the Netherlands. Neurology. 58:702\u2013708. https:\/\/doi.org\/10.1212\/WNL.58.5.702","journal-title":"Neurology."},{"key":"2714_CR8","doi-asserted-by":"publisher","first-page":"1577","DOI":"10.1093\/brain\/awp056","volume":"132","author":"AK Erichsen","year":"2009","unstructured":"Erichsen AK, Koht \u00c3J, Stray-pedersen \u00c3A, Abdelnoor M, Tallaksen CME (2009) Prevalence of hereditary ataxia and spastic paraplegia in southeast Norway\u202f: a population-based study. Brain 132:1577\u20131588. https:\/\/doi.org\/10.1093\/brain\/awp056","journal-title":"Brain"},{"key":"2714_CR9","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1002\/acn3.504","volume":"5","author":"K Reetz","year":"2018","unstructured":"Reetz K, Rodr\u00edguez R, Dogan I, Mirzazade S, Romanzetti S, Schulz JB, Cruz-Rivas EM, Alvarez-Cuesta JA, Aguilera Rodr\u00edguez R, Gonzalez Zaldivar Y, Auburger G, Vel\u00e1zquez-P\u00e9rez L (2018) Brain atrophy measures in preclinical and manifest spinocerebellar ataxia type 2. Ann Clin Transl Neurol 5:128\u2013137. https:\/\/doi.org\/10.1002\/acn3.504","journal-title":"Ann Clin Transl Neurol"},{"key":"2714_CR10","doi-asserted-by":"publisher","first-page":"1452","DOI":"10.1136\/jnnp.2003.029819","volume":"75","author":"OY Bang","year":"2004","unstructured":"Bang OY, Lee PH, Kim SY, Kim HJ, Huh K (2004) Pontine atrophy precedes cerebellar degeneration in spinocerebellar ataxia 7: MRI-based volumetric analysis. J Neurol Neurosurg Psychiatry 75:1452\u20131457. https:\/\/doi.org\/10.1136\/jnnp.2003.029819","journal-title":"J Neurol Neurosurg Psychiatry"},{"key":"2714_CR11","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1007\/s13311-018-00696-y","volume":"16","author":"RAM Buijsen","year":"2019","unstructured":"Buijsen RAM, Toonen LJA, Gardiner SL, Roon-Mom WMC (2019) Genetics, Mechanisms, and Therapeutic Progress in Polyglutamine Spinocerebellar Ataxias. Neurotherapeutics 16:263\u2013286. https:\/\/doi.org\/10.1007\/s13311-018-00696-y","journal-title":"Neurotherapeutics"},{"key":"2714_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3389\/fncel.2018.00429","volume":"12","author":"S Nethisinghe","year":"2018","unstructured":"Nethisinghe S, Lim WN, Ging H, Zeitlberger A, Abeti R, Pemble S, Sweeney MG, Labrum R, Cervera C, Houlden H, Rosser E, Limousin P, Kennedy A, Lunn MP, Bhatia KP, Wood NW, Hardy J, Polke JM, Veneziano L, Brusco A, Davis MB (2018) Complexity of the Genetics and Clinical Presentation of Spinocerebellar Ataxia 17. Front Cel Neurosci 12:1\u201310. https:\/\/doi.org\/10.3389\/fncel.2018.00429","journal-title":"Front Cel Neurosci"},{"key":"2714_CR13","doi-asserted-by":"publisher","first-page":"641","DOI":"10.1038\/nrn1474","volume":"5","author":"F Taroni","year":"2004","unstructured":"Taroni F, Didonato S (2004) Pathways to motor incoordination: the inherited ataxias. Neuroscience 5:641\u2013655. https:\/\/doi.org\/10.1038\/nrn1474","journal-title":"Neuroscience"},{"key":"2714_CR14","doi-asserted-by":"publisher","first-page":"71","DOI":"10.33588\/rn.3201.2000283","volume":"32","author":"L Vel\u00e1zquez","year":"2001","unstructured":"Vel\u00e1zquez L, Garc\u00eda R, Santos N, Paneque M, Medina E, Hechavarr\u00eda R (2001) Las ataxias hereditarias en Cuba. Aspectos hist\u00f3ricos, epidemiol\u00f3gicos, cl\u00ednicos, electrofisiol\u00f3gicos y de neurolog\u00eda cuantitativa. Rev Neurol. 32:71. https:\/\/doi.org\/10.33588\/rn.3201.2000283","journal-title":"Rev Neurol."},{"key":"2714_CR15","doi-asserted-by":"publisher","first-page":"517","DOI":"10.1517\/21678707.2015.1025747","volume":"3","author":"J Alex","year":"2015","unstructured":"Alex J, Saute M, Jardim LB (2015) Machado Joseph disease\u202f: clinical and genetic aspects, and current treatment. Expert Opin Orphan Drugs 3:517\u2013535. https:\/\/doi.org\/10.1517\/21678707.2015.1025747","journal-title":"Expert Opin Orphan Drugs"},{"key":"2714_CR16","doi-asserted-by":"crossref","unstructured":"Toyoshima Y, Onodra O, Yamada M, Tsuji S, Takahashi H (2019) Spinocerebellar Ataxia Type 17. In: Adam MP, Ardinger HH and Pagon RA (eds.) GeneReviews\u00ae [Internet]. University of Washington, Seattle","DOI":"10.1007\/978-3-319-71779-1_10"},{"key":"2714_CR17","doi-asserted-by":"publisher","first-page":"2341","DOI":"10.1093\/brain\/awl148","volume":"129","author":"K Lasek","year":"2006","unstructured":"Lasek K, Lencer R, Gaser C, Hagenah J, Walter U, Wolters A, Kock N, Steinlechner S, Nagel M, Z\u00fchlke C, Nitschke M, Brockmann K, Klein C, Rolfs A, Binkofski F (2006) Morphological basis for the spectrum of clinical deficits in spinocerebellar ataxia 17 ( SCA17). Brain 129:2341\u20132352. https:\/\/doi.org\/10.1093\/brain\/awl148","journal-title":"Brain"},{"key":"2714_CR18","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1016\/bs.irn.2018.09.011","volume":"143","author":"M Mascalchi","year":"2018","unstructured":"Mascalchi M, Vella A (2018) Neuroimaging Applications in Chronic Ataxias. Int Rev Neurobiol 143:109\u2013162. https:\/\/doi.org\/10.1016\/bs.irn.2018.09.011","journal-title":"Int Rev Neurobiol"},{"key":"2714_CR19","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1007\/s12311-014-0610-3","volume":"14","author":"L Baldar\u00e7ara","year":"2014","unstructured":"Baldar\u00e7ara L, Currie S, Hadjivassiliou M, Hoggard N, Jack A, Jackowski AP, Mascalchi M, Parazzini C, Reetz K, Righini A, Schulz JB, Vella A, Webb SJ, Habas C (2014) Consensus Paper\u202f: Radiological Biomarkers of Cerebellar Diseases. Cerebellum 14:175\u2013196. https:\/\/doi.org\/10.1007\/s12311-014-0610-3","journal-title":"Cerebellum"},{"key":"2714_CR20","doi-asserted-by":"publisher","first-page":"593","DOI":"10.1177\/028418519503600458","volume":"36","author":"SD Kumar","year":"1995","unstructured":"Kumar SD, Chand RP, Gururaj AK, Jeans WD (1995) CT features of olivopontocerebellar atrophy in children. Acta radiol 36:593\u2013596. https:\/\/doi.org\/10.1177\/028418519503600458","journal-title":"Acta radiol"},{"key":"2714_CR21","doi-asserted-by":"publisher","unstructured":"Meira AT, Arruda WO, Ono SE, Neto ADC, Raskin S, Camargo CH, Teive HAG (2019) Neuroradiological Findings in the Spinocerebellar Ataxias. Tremor and Other Hyperkinetic Movements. 1\u20138. https:\/\/doi.org\/10.7916\/tohm.v0.682","DOI":"10.7916\/tohm.v0.682"},{"key":"2714_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00401-012-1000-x","volume":"124","author":"K Seidel","year":"2012","unstructured":"Seidel K, Siswanto S, Brunt ERP, Den Dunnen W, Korf HW, R\u00fcb U (2012) Brain pathology of spinocerebellar ataxias. Acta Neuropathol 124:1\u201321. https:\/\/doi.org\/10.1007\/s00401-012-1000-x","journal-title":"Acta Neuropathol"},{"key":"2714_CR23","first-page":"1072","volume":"2","author":"Y Kim","year":"2014","unstructured":"Kim Y, Kondo M, Sunami Y, Kawata A (2014) MRI Findings in Spinocerebellar Ataxias. J Neurol Disord Stroke 2:1072","journal-title":"J Neurol Disord Stroke"},{"key":"2714_CR24","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1016\/j.neuroimage.2009.07.027","volume":"49","author":"JB Schulz","year":"2010","unstructured":"Schulz JB, Borkert J, Wolf S, Schmitz-h\u00fcbsch T, Rakowicz M, Mariotti C, Schoels L, Timmann D, Van De Warrenburg B, D\u00fcrr A, Pandolfo M, Kang J, Gonz\u00e1lez A, N\u00e4gele T, Grisoli M, Boguslawska R, Bauer P, Klockgether T, Hauser T (2010) Visualization, quanti fi cation and correlation of brain atrophy with clinical symptoms in spinocerebellar ataxia types 1, 3 and 6. Neuroimage. 49:158\u2013168. https:\/\/doi.org\/10.1016\/j.neuroimage.2009.07.027","journal-title":"Neuroimage."},{"key":"2714_CR25","doi-asserted-by":"publisher","unstructured":"Reetz K, Costa AS, Mirzazade S, Lehmann A, Juzek A, Rakowicz M, Boguslawska R, Sch\u00f6ls L, Linnemann C, Mariotti C, Grisoli M, D\u00fcrr A, Van De Warrenburg B, Timmann D, Pandolfo M, Bauer P, Jacobi H, Hauser T, Klockgether T, Schulz JB (2013) Genotype-specific patterns of atrophy progression are more sensitive than clinical decline in SCA1, SCA3 and SCA6 Kathrin. https:\/\/doi.org\/10.1093\/brain\/aws369","DOI":"10.1093\/brain\/aws369"},{"key":"2714_CR26","doi-asserted-by":"publisher","first-page":"725","DOI":"10.1111\/j.1468-1331.2005.01011.x","volume":"12","author":"A Inagaki","year":"2005","unstructured":"Inagaki A, Iida A, Matsubara M, Inagaki H (2005) Positron emission tomography and magnetic resonance imaging in spinocerebellar ataxia type 2: A study of symptomatic and asymptomatic individuals. Eur J Neurol 12:725\u2013728. https:\/\/doi.org\/10.1111\/j.1468-1331.2005.01011.x","journal-title":"Eur J Neurol"},{"key":"2714_CR27","doi-asserted-by":"publisher","first-page":"899","DOI":"10.1002\/mds.21982","volume":"23","author":"RD Nave","year":"2008","unstructured":"Nave RD, Ginestroni A, Tessa C, Cosottini M, Giannelli M, Salvatore E, Sartucci F, Michele G. De, Dotti MT, Piacentini S, Mascalchi M (2008) Brain Structural Damage in Spinocerebellar Ataxia Type 2. A Voxel-Based Morphometry Study. Mov Disord. 23:899\u2013903. https:\/\/doi.org\/10.1002\/mds.21982","journal-title":"Mov Disord."},{"key":"2714_CR28","doi-asserted-by":"publisher","first-page":"291","DOI":"10.4324\/9780429456916-3","volume":"3","author":"L Sch\u00f6ls","year":"2004","unstructured":"Sch\u00f6ls L, Bauer P, Schmidt T, Schulte T, Riess O (2004) Spinocerebellar ataxias Review Autosomal dominant cerebellar ataxias: clinical features, genetics, and pathogenesis. Decubitis 3:291\u2013304. https:\/\/doi.org\/10.4324\/9780429456916-3","journal-title":"Decubitis"},{"key":"2714_CR29","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1001\/archneur.55.1.33","volume":"55","author":"Y Murata","year":"1998","unstructured":"Murata Y, Yamaguchi S, Kawakami H, Imon Y, Maruyama H, Sakai T, Kazuta T, Ohtake T, Nishimura M, Saida T, Chiba S, Oh-I T, Nakamura S (1998) Characteristic magnetic resonance imaging findings in Machado-Joseph disease. Arch Neurol 55:33\u201337. https:\/\/doi.org\/10.1001\/archneur.55.1.33","journal-title":"Arch Neurol"},{"key":"2714_CR30","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1097\/WCO.0000000000000774","volume":"33","author":"B Soong","year":"2007","unstructured":"Soong B, Paulson HL (2007) Spincocerebellar ataxias: An update. Curr Opin Neurol 33:150\u2013160. https:\/\/doi.org\/10.1097\/WCO.0000000000000774","journal-title":"Curr Opin Neurol"},{"key":"2714_CR31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13023-016-0447-6","volume":"11","author":"A Moriarty","year":"2016","unstructured":"Moriarty A, Cook A, Hunt H, Adams ME, Cipolotti L, Giunti P (2016) A longitudinal investigation into cognition and disease progression in spinocerebellar ataxia types 1, 2, 3, 6, and 7. Orphanet J Rare Dis 11:1\u20139. https:\/\/doi.org\/10.1186\/s13023-016-0447-6","journal-title":"Orphanet J Rare Dis"},{"key":"2714_CR32","doi-asserted-by":"publisher","first-page":"1538","DOI":"10.1007\/s00415-007-0579-7","volume":"254","author":"C Mariotti","year":"2007","unstructured":"Mariotti C, Alpini D, Fancellu R, Soliveri P, Grisolo M, Ravaglia M, Lovati C, Fetoni V, Giaccone G, Castucci A, Taroni F, Gellera C, Donato SD (2007) Spinocerebellar ataxia type 17 ( SCA17): Oculomotor phenotype and clinical characterization of 15 Italian patients. J Neurol 254:1538\u20131546. https:\/\/doi.org\/10.1007\/s00415-007-0579-7","journal-title":"J Neurol"},{"key":"2714_CR33","doi-asserted-by":"publisher","unstructured":"Zhang J, Gu W, Hao Y, Chen Y (2013) Spinocerebellar ataxia 17\u202f: Inconsistency between phenotype and neuroimage findings. 16, 703\u2013704. https:\/\/doi.org\/10.4103\/0972-2327.120457","DOI":"10.4103\/0972-2327.120457"},{"key":"2714_CR34","doi-asserted-by":"publisher","first-page":"1521","DOI":"10.1002\/mds.20529","volume":"20","author":"CT Loy","year":"2005","unstructured":"Loy CT, Epi MC, Sweeney MG, Davis MB, Wills AJ, Sawle GV, Lees AJ, Tabrizi SJ (2005) Spinocerebellar Ataxia Type 17: Extension of Phenotype With Putaminal Rim Hyperintensity on Magnetic Resonance Imaging. Mov Disord 20:1521\u20131528. https:\/\/doi.org\/10.1002\/mds.20529","journal-title":"Mov Disord"},{"key":"2714_CR35","doi-asserted-by":"publisher","unstructured":"Carroll LS, Massey TH, Wardle M, Peall KJ (2018) Dentatorubral-pallidoluysian Atrophy\u202f: An Update. Tremor and Other Hyperkinetic Movements. 8, https:\/\/doi.org\/10.7916\/D81N9HST","DOI":"10.7916\/D81N9HST"},{"key":"2714_CR36","doi-asserted-by":"publisher","first-page":"322","DOI":"10.1007\/s00415-017-8705-7","volume":"265","author":"A Sugiyama","year":"2017","unstructured":"Sugiyama A, Sato N, Nakata Y, Kimura Y, Enokizono M (2017) Clinical and magnetic resonance imaging features of elderly onset dentatorubral \u2013 pallidoluysian atrophy. J Neurol 265:322\u2013329. https:\/\/doi.org\/10.1007\/s00415-017-8705-7","journal-title":"J Neurol"},{"key":"2714_CR37","unstructured":"Shao F, Xie X (2013) An overview on interactive medical image segmentation. Annals of the BMWA 2013(7):1\u201322"},{"key":"2714_CR38","doi-asserted-by":"crossref","unstructured":"Van Der Lijn F, De Bruijne M, Hoogendam YY, Klein S, Hameeteman R, Breteler MMB, Niessen WJ (2009)\u00a0Cerebellum segmentation in MRI using atlas registration and local multi-scale image descriptors. In: 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro. pp. 221\u2013224","DOI":"10.1109\/ISBI.2009.5193023"},{"key":"2714_CR39","doi-asserted-by":"publisher","first-page":"425","DOI":"10.1016\/S0730-725X(02)00508-8","volume":"20","author":"N Saeed","year":"2002","unstructured":"Saeed N, Puri BK (2002) Cerebellum segmentation employing texture properties and knowledge based image processing\u202f: applied to normal adult controls and patients. Magn Reson Imaging 20:425\u2013429","journal-title":"Magn Reson Imaging"},{"key":"2714_CR40","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1016\/j.neuroimage.2012.08.009","volume":"65","author":"MJ Cardoso","year":"2013","unstructured":"Cardoso MJ, Melbourne A, Kendall GS, Modat M, Robertson NJ, Marlow N, Ourselin S (2013) AdaPT\u202f: An adaptive preterm segmentation algorithm for neonatal brain MRI. Neuroimage 65:97\u2013108. https:\/\/doi.org\/10.1016\/j.neuroimage.2012.08.009","journal-title":"Neuroimage"},{"key":"2714_CR41","doi-asserted-by":"publisher","first-page":"584","DOI":"10.1162\/089892903321662967","volume":"15","author":"N Makris","year":"2003","unstructured":"Makris N, Hodge SM, Haselgrove C, Kennedy DN, Dale A, Fischl B, Rosen BR, Harris G, Caviness VS, Schmahmann JD (2003) Human Cerebellum\u202f: Surface-Assisted Cortical Parcellation and Volumetry with Magnetic Resonance Imaging. J Cogn Neurosci 15:584\u2013599","journal-title":"J Cogn Neurosci"},{"key":"2714_CR42","doi-asserted-by":"publisher","first-page":"916","DOI":"10.1016\/j.neuroimage.2016.11.003","volume":"147","author":"J Romero","year":"2016","unstructured":"Romero J, Coup\u00e9 P, Giraud R, Ta V, Fonov V, Park MT, Chakravarty M, Voineskos A, Manj\u00f3n J (2016) CERES\u202f: A new cerebellum lobule segmentation method. Neuroimage 147:916\u2013924","journal-title":"Neuroimage"},{"key":"2714_CR43","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1016\/j.neuroimage.2006.05.056","volume":"33","author":"J Diedrichsen","year":"2006","unstructured":"Diedrichsen J (2006) A spatially unbiased atlas template of the human cerebellum. Neuroimage 33:127\u2013138. https:\/\/doi.org\/10.1016\/j.neuroimage.2006.05.056","journal-title":"Neuroimage"},{"key":"2714_CR44","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1016\/j.neuroimage.2018.08.003.Comparing","volume":"183","author":"A Carass","year":"2018","unstructured":"Carass A, Cuzzocreo JL, Han S, Hernandez-castillo CR, Rasser PE, Ganz M, Beliveau V, Dolz J, Ayed IB, Desrosiers C, Thyreau B, Fonov VS, Collins DL, Ying SH, Onyike CU, Landman BA, Mostofsky SH, Thompson PM, Prince JL (2018) Comparing fully automated state-of-the-art cerebellum parcellation from magnetic resonance images. Neuroimage. 183:150\u2013172. https:\/\/doi.org\/10.1016\/j.neuroimage.2018.08.003.Comparing","journal-title":"Neuroimage."},{"key":"2714_CR45","doi-asserted-by":"crossref","unstructured":"De Br\u00e9bisson A, Montana G (2015) Deep Neural Networks for Anatomical Brain Segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition works. pp. 20\u201328","DOI":"10.1109\/CVPRW.2015.7301312"},{"key":"2714_CR46","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1162\/neco.1989.1.4.541","volume":"1","author":"Y LeCun","year":"1989","unstructured":"LeCun Y, Boser B, Denker JS, Henderson D, Howard RE, Hubbard W, Jackel LD (1989) Backpropagation applied to digit recognition. Neural Comput 1:541\u2013551","journal-title":"Neural Comput"},{"key":"2714_CR47","doi-asserted-by":"publisher","first-page":"818","DOI":"10.1016\/j.ancr.2017.02.001","volume":"12","author":"MD Zeiler","year":"2014","unstructured":"Zeiler MD, Fergus R (2014) Visualizing and Understanding Convolutional Networks. Anal Chem Res 12:818\u2013833. https:\/\/doi.org\/10.1016\/j.ancr.2017.02.001","journal-title":"Anal Chem Res"},{"key":"2714_CR48","doi-asserted-by":"publisher","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 36:193\u2013202","journal-title":"Biol Cybern"},{"key":"2714_CR49","doi-asserted-by":"crossref","unstructured":"Hariharan B, Arbel\u00e1ez P, Bourdev L, Maji S, Malik J (2011) Semantic Contours from Inverse Detectors. In: International Conference on Computer Vision. pp. 991\u2013998","DOI":"10.1109\/ICCV.2011.6126343"},{"key":"2714_CR50","doi-asserted-by":"crossref","unstructured":"Wang L, Ouyang W, Wang X, Lu H (2015) Visual Tracking with Fully Convolutional Networks. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 3119\u20133127","DOI":"10.1109\/ICCV.2015.357"},{"key":"2714_CR51","unstructured":"Jaroensri R, Zhao A, Balakrishnan G, Lo D, Schmahmann JD, Durand F, Guttag J (2017) A Video-Based Method for Automatically Rating Ataxia. In: Proceedings of Machine Learning. pp. 1\u201313"},{"key":"2714_CR52","unstructured":"El Amin AM, Liu Q, Wang Y (2016) Convolutional neural network features based change detection in satellite images. First International Workshop on Pattern Recognition. 10011. pp. 181\u2013186"},{"key":"2714_CR53","doi-asserted-by":"publisher","first-page":"1038","DOI":"10.1016\/j.neuroimage.2016.09.046","volume":"146","author":"C Kawahara","year":"2017","unstructured":"Kawahara C, Brown CJ, Miller SP, Booth BG, Chau V, Grunau RE, Zwicker JG, Hamarneh G (2017) BrainNetCNN\u202f: Convolutional Neural Networks for Brain Networks Towards Predicting Neurodevelopment. Neuroimage 146:1038\u20131049","journal-title":"Neuroimage"},{"key":"2714_CR54","doi-asserted-by":"publisher","first-page":"3032","DOI":"10.3390\/s20113032","volume":"20","author":"C Stoean","year":"2020","unstructured":"Stoean C, Stoean R, Atencia M, Abdar M, Vel\u00e1zquez-P\u00e9rez L, Khosrabi A, Nahavandi S, Acharya UR, Joya G (2020) Automated Detection of Presymptomatic Conditions in Spinocerebellar Ataxia Type 2 Using Monte Carlo Dropout and Deep Neural Network Techniques with Electrooculogram Signals. Sensors 20:3032. https:\/\/doi.org\/10.3390\/s20113032","journal-title":"Sensors"},{"key":"2714_CR55","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1016\/j.patcog.2017.10.013","volume":"77","author":"J Gu","year":"2018","unstructured":"Gu J, Wang Z, Kuen J, Ma L, Shahroudy A, Shuai B, Liu T, Wang X, Wang L, Wang G, Gai J, Chen T (2018) Recent Advances in Convolutional Neural Networks. Pattern Recognit 77:354\u2013377","journal-title":"Pattern Recognit"},{"key":"2714_CR56","doi-asserted-by":"publisher","unstructured":"Milletari F, Navab N, Ahmadi SA (2016) V-Net: Fully convolutional neural networks for volumetric medical image segmentation. Proc. - 2016 4th Int. Conf. 3D Vision, 3DV 2016. 565\u2013571. https:\/\/doi.org\/10.1109\/3DV.2016.79","DOI":"10.1109\/3DV.2016.79"},{"key":"2714_CR57","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1016\/j.cviu.2017.04.002","volume":"164","author":"F Milletari","year":"2016","unstructured":"Milletari F, Ahmadi SA, Kroll C, Plate A, Rozanski V, Maiostre J, Levin J, Dietrich O, Ertl-Wagner B, B\u00f6tzel K, Navab N (2016) Hough-CNN: Deep learning for segmentation of deep brain regions in MRI and ultrasound. Comput Vis Image Underst 164:92\u2013102. https:\/\/doi.org\/10.1016\/j.cviu.2017.04.002","journal-title":"Comput Vis Image Underst"},{"key":"2714_CR58","unstructured":"Wu J (2017)\u00a0 Introduction to Convolutional Neural Networks. https:\/\/web.archive.org\/web\/20180928011532\/https:\/\/cs.nju.edu.cn\/wujx\/teaching\/15_CNN.pdf. Accessed 23 July 2022"},{"key":"2714_CR59","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1201\/9781420010749","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2017) ImageNet Classification with Deep Convolutional Neural networks. Commun ACM 60:84\u201390. https:\/\/doi.org\/10.1201\/9781420010749","journal-title":"Commun ACM"},{"key":"2714_CR60","unstructured":"Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. 3rd Int Conf Learn Represent ICLR 2015 - Conf Track Proc. 1\u201314"},{"key":"2714_CR61","doi-asserted-by":"publisher","unstructured":"Zeiler MD, Taylor GW, Fergus R (2011) Adaptive deconvolutional networks for mid and high level feature learning. Proc IEEE Int Conf Comput Vis. 2018\u20132025. https:\/\/doi.org\/10.1109\/ICCV.2011.6126474","DOI":"10.1109\/ICCV.2011.6126474"},{"key":"2714_CR62","first-page":"191","volume":"7","author":"AF Yaseen","year":"2018","unstructured":"Yaseen AF (2018) A Survey on the Layers of Convolutional Neural Networks. Int J Comput Sci Mob Comput 7:191\u2013196","journal-title":"Int J Comput Sci Mob Comput"},{"key":"2714_CR63","first-page":"28","volume":"3","author":"BAM Ashqar","year":"2019","unstructured":"Ashqar BAM, Abu-Naser SS (2019) Identifying Images of Invasive Hydrangea Using Pre-Trained Deep Convolutional Neural Networks. Int J Acad Eng Res 3:28\u201336","journal-title":"Int J Acad Eng Res"},{"key":"2714_CR64","doi-asserted-by":"publisher","unstructured":"Han S, Carass A, He Y, Prince JL (2020) Automatic Cerebellum Anatomical Parcellation using U-Net with Locally Constrained Optimization. IEEE Trans. Med. Imaging. 116819. https:\/\/doi.org\/10.1016\/j.neuroimage.2020.116819","DOI":"10.1016\/j.neuroimage.2020.116819"},{"key":"2714_CR65","doi-asserted-by":"publisher","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics). 9351, 234\u2013241. https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"2714_CR66","doi-asserted-by":"crossref","unstructured":"Hariharan B, Arbel\u00e1ez P, Girshick R, Malik J (2014) Simultaneous detection and segmentation. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). pp. 297\u2013312","DOI":"10.1007\/978-3-319-10584-0_20"},{"key":"2714_CR67","unstructured":"Pathak D, Shelhamer E, Long J, Darrell T (2014) Fully Convolutional Multi-Class Multiple Instance Learning. arXiv Prepr. arXiv1412.7144"},{"key":"2714_CR68","doi-asserted-by":"publisher","unstructured":"Han S, He Y, Carass A, Ying SH, Prince JL (2019) Cerebellum Parcellation with Convolutional Neural Networks. Proc SPIE Int Soc Opt Eng. 10949,\u00a0https:\/\/doi.org\/10.1117\/12.2512119.Cerebellum","DOI":"10.1117\/12.2512119.Cerebellum"},{"key":"2714_CR69","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3389\/fncom.2019.00044","volume":"13","author":"DE Cahall","year":"2019","unstructured":"Cahall DE, Rasool G, Bouaynaya NC, Fathallah-Shaykh HM (2019) Inception Modules Enhance Brain Tumor Segmentation. Front Comput Neurosci 13:1\u20138. https:\/\/doi.org\/10.3389\/fncom.2019.00044","journal-title":"Front Comput Neurosci"},{"key":"2714_CR70","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1007\/978-3-030-11726-9","volume":"2","author":"M Marcinkiewicz","year":"2019","unstructured":"Marcinkiewicz M, Nalepa J, Lorenzo PR, Dudzik W, Mrukwa G (2019) Segmenting Brain Tumors from MRI Using Cascaded Multi-modal U-Nets. Springer Nat Switz 2:13\u201324. https:\/\/doi.org\/10.1007\/978-3-030-11726-9","journal-title":"Springer Nat Switz"},{"key":"2714_CR71","doi-asserted-by":"crossref","unstructured":"Qamar S, Ahmad P, Shen L (2020) HI-Net: Hyperdense Inception 3 D UNet for Brain Tumor Segmentation. In: International Conference on Medical Image Computing and Computer Assisted Intervention. pp. 1\u20139","DOI":"10.1007\/978-3-030-72087-2_5"},{"key":"2714_CR72","doi-asserted-by":"crossref","unstructured":"Huang G, Van Der Maaten L, Weinberger KQ (2017) Densely Connected Convolutional Networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 4700\u20134708","DOI":"10.1109\/CVPR.2017.243"},{"key":"2714_CR73","doi-asserted-by":"publisher","first-page":"101550","DOI":"10.1109\/ACCESS.2020.2998537","volume":"8","author":"J Kim","year":"2020","unstructured":"Kim J, Patriat R, Kaplan J, Solomon O, Harel N (2020) Deep Cerebellar Nuclei Segmentation via Semi-Supervised Deep Context-Aware Learning from 7T Diffusion MRI. IEEE Access 8:101550\u2013101568. https:\/\/doi.org\/10.1109\/ACCESS.2020.2998537","journal-title":"IEEE Access"},{"key":"2714_CR74","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1016\/j.procs.2018.10.327","volume":"140","author":"RD Gottapu","year":"2018","unstructured":"Gottapu RD, Dagli CH (2018) DenseNet for Anatomical Brain Segmentation. Procedia Comput Sci 140:179\u2013185. https:\/\/doi.org\/10.1016\/j.procs.2018.10.327","journal-title":"Procedia Comput Sci"},{"key":"2714_CR75","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep Residual Learning for Image Recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 1\u20139","DOI":"10.1109\/CVPR.2016.90"},{"key":"2714_CR76","doi-asserted-by":"crossref","unstructured":"Mehta R, Sivaswamy J (2017) M-NET\u202f: A Convolutional Neural Network for Deep Brain Structure Segmentation. In: 2017 IEEE International Symposium on Biomedical Imaging. pp. 437\u2013440","DOI":"10.1109\/ISBI.2017.7950555"},{"key":"2714_CR77","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TMI.2018.2835303","volume":"37","author":"L Chen","year":"2018","unstructured":"Chen L, Bentley P, Mori K, Misawa K, Fujiwara M, Rueckert D (2018) DRINet for Medical Image Segmentation. IEEE Trans Med Imaging 37:1\u201311","journal-title":"IEEE Trans Med Imaging"},{"key":"2714_CR78","doi-asserted-by":"crossref","unstructured":"Landmann BA, Hyang AJ, Gifford A, Vikram DS, Lim IAL, Farrell JAD, Bogovic JA, Hua J, Chen M, Jarso S, Smith SA, Joel S, Mori S, Pekar JJ, Barker PB, Prince JL, van Zijl PCM (2012) Multi-parametric neuroimaging reproducibility: A 3T resource study. Neuroimage 54(4):2854\u20132866","DOI":"10.1016\/j.neuroimage.2010.11.047"},{"key":"2714_CR79","unstructured":"Internet Brain Segmentation Repository (IBSR). https:\/\/www.nitrc.org\/projects\/ibsr. Accessed 29 Oct 2022"},{"key":"2714_CR80","unstructured":"Asman A, Akhondi-Asl A, Wang H, Tustison N, Avants B, Warfield S, Landman B (2013) Miccai 2013 segmentation algorithms, theory and applications (SATA) challenge results summary. In: MICCAI Challenge Workshop on Segmentation: Algorithms, Theory and Applications (SATA)"},{"key":"2714_CR81","doi-asserted-by":"publisher","unstructured":"Mehta R, Majumdar A, Sivaswamy J (2017) BrainSegNet: a convolutional neural network architecture for automated segmentation of human brain structures BrainSegNet\u202f: a convolutional neural network architecture for automated segmentation of. J Med Imaging. 4. https:\/\/doi.org\/10.1117\/1.JMI.4.2.024003","DOI":"10.1117\/1.JMI.4.2.024003"},{"key":"2714_CR82","doi-asserted-by":"publisher","first-page":"1064","DOI":"10.1016\/j.neuroimage.2007.09.031.Construction","volume":"39","author":"DW Shattuck","year":"2008","unstructured":"Shattuck DW, Mirza M, Adisetiyo V, Hojatkashani C, Narr KL, Poldrack RA, Bilder RM, Arthur W (2008) Construction of a 3D Probabilistic Atlas of Human Cortical Structures. Neuroimage 39:1064\u20131080. https:\/\/doi.org\/10.1016\/j.neuroimage.2007.09.031.Construction","journal-title":"Neuroimage"},{"key":"2714_CR83","unstructured":"Brain Development Webpage. https:\/\/brain-development.org\/brain-atlases\/. Accessed 26 Oct 2022"},{"key":"2714_CR84","doi-asserted-by":"crossref","unstructured":"Moeskops P, Veta M, Lafarge MW, Eppenhof KAJ, Pluim JPW (2017) Adversarial training and dilated convolutions for brain MRI segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. pp. 56\u201364. Springer","DOI":"10.1007\/978-3-319-67558-9_7"},{"key":"2714_CR85","doi-asserted-by":"crossref","unstructured":"Nguyen DMH, Vu HT, Ung HQ, Nguyen BT (2017) 3D-brain segmentation using deep neural network and Gaussian mixture model. In: 2017 IEEE Winter Conference on Applications of Computer Vision. pp. 815\u2013824","DOI":"10.1109\/WACV.2017.96"},{"key":"2714_CR86","doi-asserted-by":"publisher","first-page":"446","DOI":"10.1016\/j.neuroimage.2017.04.041","volume":"170","author":"H Chen","year":"2018","unstructured":"Chen H, Dou Q, Yu L, Qin J, Heng P (2018) VoxResNet\u202f: Deep voxelwise residual networks for brain segmentation from 3D MR images. Neuroimage 170:446\u2013455. https:\/\/doi.org\/10.1016\/j.neuroimage.2017.04.041","journal-title":"Neuroimage"},{"key":"2714_CR87","doi-asserted-by":"crossref","unstructured":"Mendrik AM, Vincken KL, Kuijf HJ, Breeuwer M, Bouvy WH, De Bresser J, Alansary A, De Bruijne M, Carass A, El-baz A, Jog A, Katyal R, Khan AR, Van Der Lijn F, Mahmood Q, Mukherjee R, Van Opbroek A, Paneri S, Pereira S, Persson M, Rajchl M, Sarikaya D, Smedby \u00d6, Silva CA, Vrooman HA, Vyas S, Wang C, Zhao L, Biessels GJ, Viergever MA (2015) MRBrainS Challenge\u202f: Online Evaluation Framework for Brain Image Segmentation in 3T MRI Scans. Comput. Intell. Neurosci. 2015","DOI":"10.1155\/2015\/813696"},{"key":"2714_CR88","doi-asserted-by":"crossref","unstructured":"Manoharan H, Pang G, Wu H (2019) Visualization of MRI Datasets for Anatomical Brain Segmentation by Pixel-level Analysis. In: 2019 IEEE 10th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON). pp. 562\u2013568. IEEE","DOI":"10.1109\/IEMCON.2019.8936302"},{"key":"2714_CR89","unstructured":"Lei Z, Qi L, Wei Y, Zhou Y, Qi W (2019) Infant Brain MRI Segmentation with Dilated Convolution Pyramid Down-sampling and Self-attention. arXiv Prepr. arXiv1912.12570. 1\u20139"},{"key":"2714_CR90","doi-asserted-by":"publisher","first-page":"2219","DOI":"10.1109\/TMI.2019.2901712","volume":"38","author":"L Wang","year":"2020","unstructured":"Wang L, Nie D, Li G, Dolz J, Technologie ED, Zhang Q, Wang F, Xia J, Wu Z, Chen J (2020) Benchmark on Automatic 6-month-old Infant Brain Segmentation Algorithms: The iSeg-2017 Challenge. IEEE Trans Med Imaging 38:2219\u20132230","journal-title":"IEEE Trans Med Imaging"},{"key":"2714_CR91","doi-asserted-by":"publisher","first-page":"1363","DOI":"10.1109\/TMI.2021.3055428","volume":"40","author":"Y Sun","year":"2019","unstructured":"Sun Y, Gao K, Wu Z, Lei Z, Wei Y, Ma J, Yang X, Feng X, Zhao L, Le T, Shin J, Zhong T, Zhang Y, Yu L, Li C, Basnet R, Ahmad MO, Swamy MNS, Ma W, Dou Q, Bui TD, Noguera CB, Landman B, Member S, Ian H, Humphreys KL, Shultz S, Li L, Niu S, Lin W, Jewells V, Li G, Shen D, Wang L (2019) Multi-Site Infant Brain Segmentation Algorithms: The iSeg-2019 Challenge. IEEE Trans Med Imaging 40:1363\u20131376","journal-title":"IEEE Trans Med Imaging"},{"key":"2714_CR92","doi-asserted-by":"publisher","unstructured":"Thyreau B, Taki Y (2020) Learning a cortical parcellation of the brain robust to the MRI segmentation with convolutional neural networks. Med Image Anal. 61. https:\/\/doi.org\/10.1016\/j.media.2020.101639","DOI":"10.1016\/j.media.2020.101639"},{"key":"2714_CR93","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.media.2016.10.004","volume":"36","author":"K Kamnitsas","year":"2016","unstructured":"Kamnitsas K, Ledig C, Newcombe VFJ, Simpson JP, Kane AD, Menon DK, Rueckert D, Glocker B (2016) Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med Image Anal 36:61\u201378. https:\/\/doi.org\/10.1016\/j.media.2016.10.004","journal-title":"Med Image Anal"},{"key":"2714_CR94","unstructured":"Open Data Commons for Traumatic Brain Injury. https:\/\/odc-tbi.org\/. Accessed 26 Oct 2022"},{"key":"2714_CR95","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, Pal C, Jodoin P, Larochelle H (2017) Brain Tumor Segmentation with Deep Neural Networks. Med Image Anal 35:18\u201331","journal-title":"Med Image Anal"},{"key":"2714_CR96","first-page":"785","volume":"11","author":"SA Bala","year":"2020","unstructured":"Bala SA, Kant S (2020) Dense Dilated Inception Network for Medical Image Segmentation. Int J Adv Comput Sci Appl 11:785\u2013793","journal-title":"Int J Adv Comput Sci Appl"},{"key":"2714_CR97","doi-asserted-by":"crossref","unstructured":"Farooq A, Anwar SM, Awais M, Rehman S (2017) A Deep CNN based Multi-class Classification on Alzheimer\u2019s Disease using MRI. In: 2017 IEEE International Conference on Imaging systems and techniques (IST). pp. 1\u20136","DOI":"10.1109\/IST.2017.8261460"},{"key":"2714_CR98","doi-asserted-by":"publisher","unstructured":"Jr CRJ, Bernstein MA, Fox NC, Thompson P, Alexander G, Harvey D, Borowski B, Britson PJ, Whitwell JL, Ward C, Dale AM, Felmlee JP, Gunter JL, Hill DLG, Killiany R, Schuff N, Fox-bosetti S, Lin C, Studholme C, Decarli CS, Krueger G, Ward HA, Metzger GJ, Scott KT, Mallozzi R, Blezek D, Levy J, Debbins JP, Fleisher AS, Albert M, Green R, Bartzokis G, Glover G, Mugler J, Weiner MW (2008) The Alzheimer \u2019 s Disease Neuroimaging Initiative ( ADNI ): MRI Methods. 691, 685\u2013691. https:\/\/doi.org\/10.1002\/jmri.21049","DOI":"10.1002\/jmri.21049"},{"key":"2714_CR99","doi-asserted-by":"publisher","first-page":"176","DOI":"10.1016\/j.cogsys.2018.12.007","volume":"54","author":"M Talo","year":"2019","unstructured":"Talo M, Baloglu UB, Yildrim \u00d6, Acharya UR (2019) Application of deep transfer learning for automated brain abnormality classification using MR images. Cogn Syst Res 54:176\u2013188","journal-title":"Cogn Syst Res"},{"key":"2714_CR100","unstructured":"Harvard Medical School Data. http:\/\/www.med.harvard.edu\/AANLIB\/. Accessed: 26 Oct 2022"},{"key":"2714_CR101","doi-asserted-by":"publisher","first-page":"196","DOI":"10.3906\/elk-1904-172","volume":"28","author":"A Y\u0130\u011f\u0130t","year":"2020","unstructured":"Y\u0130\u011f\u0130t A, I\u015fik Z (2020) Applying deep learning models to structural MRI for stage prediction of Alzheimer \u2019 s disease. Turkish J Electr Eng Comput Sci. 28:196\u2013210. https:\/\/doi.org\/10.3906\/elk-1904-172","journal-title":"Turkish J Electr Eng Comput Sci."},{"key":"2714_CR102","doi-asserted-by":"crossref","unstructured":"Marcus DS, Wang TH, Parker J, Csernansky JG, Morris JC, Buckner RL (2021) Open Access Series of Imaging Studies ( OASIS ): Cross-sectional MRI Data in Young , Middle Aged , Nondemented , and Demented Older Adults. 1498\u20131507","DOI":"10.1162\/jocn.2007.19.9.1498"},{"key":"2714_CR103","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1016\/j.neuroimage.2012.12.044","volume":"70","author":"IB Malone","year":"2013","unstructured":"Malone IB, Cash D, Ridgway GR, MacManus DG, Ourselin S, Fox NC, Schott JM (2013) MIRIAD-Public release of a multiple time point Alzheimer\u2019s MR imaging dataset. Neuroimage 70:33\u201336. https:\/\/doi.org\/10.1016\/j.neuroimage.2012.12.044","journal-title":"Neuroimage"},{"key":"2714_CR104","doi-asserted-by":"publisher","first-page":"456","DOI":"10.1016\/j.neuroimage.2017.04.039","volume":"170","author":"J Dolz","year":"2018","unstructured":"Dolz J, Desrosiers C, Ben Ayed I (2018) 3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study. Neuroimage 170:456\u2013470. https:\/\/doi.org\/10.1016\/j.neuroimage.2017.04.039","journal-title":"Neuroimage"},{"key":"2714_CR105","doi-asserted-by":"publisher","first-page":"707","DOI":"10.1093\/aww348","volume":"140","author":"K Kansal","year":"2017","unstructured":"Kansal K, Yang Z, Fishman AM, Sair HI, Ying SH, Jedynak BM, Prince JL, Onyike CU (2017) Structural cerebellar correlates of cognitive and motor dysfunctions in cerebellar degeneration. Brain 140:707\u2013720. https:\/\/doi.org\/10.1093\/aww348","journal-title":"Brain"},{"key":"2714_CR106","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2017.10","volume":"4","author":"AD Martino","year":"2017","unstructured":"Martino AD, Connor DO, Chen B, Alaerts K, Anderson JS, Assaf M, Balsters JH, Baxter L, Beggiato A, Bernaerts S, Blanken LME, Bookheimer SY, Braden BB, Byrge L, Castellanos FX, Dapretto M, Delorme R, Fair DA, Fishman I, Fitzgerald J, Gallagher L, Keehn RJJ, Kennedy DP, Lainhart JE, Luna B, Mostofsky SH, M\u00fcller R-A, Nebel MB, Nigg JT, O\u2019Hearn K, Solomon M, Toro R, Vaidya CJ, Wenderoth N, White T, Craddock RC, Lord C, Leventhal B, Milham MP (2017) Data Descriptor: Enhancing studies of the connectome in autism using the autism brain imaging data exchange II. Sci Data 4:1\u201315","journal-title":"Sci Data"},{"key":"2714_CR107","unstructured":"Simon J, Drozdzal M, Vazquez D, Romero A, Bengio Y (2017) The One Hundred Layers Tiramisu\u202f: Fully Convolutional DenseNets for Semantic Segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops. pp. 11\u201319"},{"key":"2714_CR108","unstructured":"Yu F, Koltun V (2016) Multi-Scale Context Aggregation by Dilated Convolutions. In: Proceedings of the International Conference on Learning Representations. pp. 1\u201313"},{"key":"2714_CR109","unstructured":"Pleiss G, Chen D, Huang G, Li T, van der Maaten L, Weinberger KQ (2017) Memory-Efficient Implementation of DenseNets. arXiv Prepr. arXiv1707.06990"},{"key":"2714_CR110","unstructured":"Bjorck J, Gomes C, Selman B, Weinberger KQ (2018) Understanding batch normalization. Adv. Neural Inf. Process. Syst. 2018-Decem, 7694\u20137705"},{"key":"2714_CR111","doi-asserted-by":"crossref","unstructured":"Ulyanov D, Vedaldi A, Lempitsky V (2017) Improved Texture Networks\u202f: Maximizing Quality and Diversity in Feed-forward Stylization and Texture Synthesis. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 6924\u20136932","DOI":"10.1109\/CVPR.2017.437"},{"key":"2714_CR112","doi-asserted-by":"publisher","first-page":"993","DOI":"10.1016\/j.patcog.2014.08.027","volume":"48","author":"P Mukhopadhyay","year":"2015","unstructured":"Mukhopadhyay P, Chaudhuri BB (2015) A survey of Hough Transform. Pattern Recognit 48:993\u20131010. https:\/\/doi.org\/10.1016\/j.patcog.2014.08.027","journal-title":"Pattern Recognit"},{"key":"2714_CR113","doi-asserted-by":"crossref","unstructured":"Banfield JD, Raftery AE (1993) Model-based Gaussian and non-Gaussian Clustering 0. Biometrics 49(3):803\u2013821","DOI":"10.2307\/2532201"},{"key":"2714_CR114","unstructured":"Goodfellow IJ, Warde-Farley D, Mirza M, Courville A, Bengio Y (2013) Maxout Networks. In: International Conference on Machine Learning. pp. 1319\u20131327"},{"key":"2714_CR115","doi-asserted-by":"publisher","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","volume":"34","author":"BH Menze","year":"2015","unstructured":"Menze BH, Jakab A, Bauer S, Kalpathy-cramer J, Farahani K, Kirby J, Burren Y, Porz N, Slotboom J, Wiest R, Lanczi L, Gerstner E, Weber M, Arbel T, Avants BB, Ayache N, Buendia P, Collins DL, Cordier N, Corso JJ, Criminisi A, Das T, Delingette H, Demiralp \u00c7, Durst CR, Dojat M, Doyle S, Festa J, Forbes F, Geremia E, Glocker B, Golland P, Guo X, Hamamci A, Iftekharuddin KM, Jena R, John NM, Konukoglu E, Lashkari D, Mariz JA, Meier R, Pereira S, Precup D, Price SJ, Raviv TR, Reza SMS, Ryan M, Sarikaya D, Schwartz L, Shin H, Shotton J, Silva CA, Sousa N, Subbanna NK, Szekely G, Taylor TJ, Thomas OM, Tustison NJ, Unal G, Vasseur F, Wintermark M, Ye DH, Zhao L, Zhao B, Zikic D, Prastawa M, Reyes M, Leemput KV (2015) The Multimodal Brain Tumor Image Segmentation Benchmark ( BRATS ). IEEE Trans Med Imaging 34:1993\u20132024. https:\/\/doi.org\/10.1109\/TMI.2014.2377694","journal-title":"IEEE Trans Med Imaging"},{"key":"2714_CR116","unstructured":"Bakas S, Reyes M, Jakab A, Bauer S, Rempfler M, Crimi A, Shinohara RT, Berger C, Ha SM, Rozycki M, Prastawa M, Alberts E, Lipkova J, Freymann J, Kirby J, Bilello M, Fathallah-Shaykh H, Wiest R, Kirschke J, Wiestler B, Colen R, Kotrotsou A, Lamontagne P, Marcus D, Milchenko M, Nazeri A, Weber M-A, Mahajan A, Baid U, Gerstner E, Kwon D, Acharya G, Agarwal M, Alam M, Albiol A, Albiol A, Albiol FJ, Alex V, Allinson N, Amorim PHA, Amrutkar A, Anand G, Andermatt S, Arbel T, Arbelaez P, Avery A, Azmat MBP, Bai W, Banerjee S, Barth B, Batchelder T, Batmanghelich K, Battistella E, Beers A, Belyaev M, Bendszus M, Benson E, Bernal J, Bharath HN, Biros G, Bisdas S, Brown J, Cabezas M, Cao S, Cardoso JM, Carver EN, Casamitjana A, Castillo LS, Cat\u00e0 M, Cattin P, Cerigues A, Chagas VS, Chandra S, Chang Y-J, Chang S, Chang K, Chazalon J, Chen S, Chen W, Chen JW, Chen Z, Cheng K, Choudhury AR, Chylla R, Cl\u00e9rigues A, Colleman S, Colmeiro RGR, Combalia M, Costa A, Cui X, Dai Z, Dai L, Daza LA, Deutsch E, Ding C, Dong C, Dong S, Dudzik W, Eaton-Rosen Z, Egan G, Escudero G, Estienne T, Everson R, Fabrizio J, Fan Y, Fang L, Feng X, Ferrante E, Fidon L, Fischer M, French AP, Fridman N, Fu H, Fuentes D, Gao Y, Gates E, Gering D, Gholami A, Gierke W, Glocker B, Gong M, Gonz\u00e1lez-Vill\u00e1 S, Grosges T, Guan Y, Guo S, Gupta S, Han W-S, Han IS, Harmuth K, He H, Hern\u00e1ndez-Sabat\u00e9 A, Herrmann E, Himthani N, Hsu W, Hsu C, Hu X, Hu X, Hu Y, Hu Y, Hua R, Huang T-Y, Huang W, Van Huffel S, Huo QHVV, Iftekharuddin KM, Isensee F, Islam M, Jackson AS, Jambawalikar SR, Jesson A, Jian W, Jin P, Jose VJM, Jungo A, Kainz B, Kamnitsas K, Kao P-Y, Karnawat A, Kellermeier T, Kermi A, Keutzer K, Khadir MT, Khened M, Kickingereder P, Kim G, King N, Knapp H, Knecht U, Kohli L, Kong D, Kong X, Koppers S, Kori A, Krishnamurthi G, Krivov E, Kumar P, Kushibar K, Lachinov D, Lambrou T, Lee J, Lee C, Lee Y, Lee M, Lefkovits S, Lefkovits L, Levitt J, Li T, Li H, Li W, Li H, Li X, Li Y, Li H, Li Z, Li X, Li Z, Li X, Li W, Lin Z-S, Lin F, Lio P, Liu C, Liu B, Liu X, Liu M, Liu J, Liu L, Llado X, Lopez MM, Lorenzo PR, Lu Z, Luo L, Luo Z, Ma J, Ma K, Mackie T, Madabushi A, Mahmoudi I, Maier-Hein KH, Maji P, Mammen C, Mang A, Manjunath BS, Marcinkiewicz M, McDonagh S, McKenna S, McKinley R, Mehl M, Mehta S, Mehta R, Meier R, Meinel C, Merhof D, Meyer C, Miller R, Mitra S, Moiyadi A, Molina-Garcia D, Monteiro MAB, Mrukwa G, Myronenko A, Nalepa J, Ngo T, Nie D, Ning H, Niu C, Nuechterlein NK, Oermann E, Oliveira A, Oliveira DDC, Oliver A, Osman AFI, Ou Y-N, Ourselin S, Paragios N, Park MS, Paschke B, Pauloski JG, Pawar K, Pawlowski N, Pei L, Peng S, Pereira SM, Perez-Beteta J, Perez-Garcia VM, Pezold S, Pham B, Phophalia A, Piella G, Pillai GN, Piraud M, Pisov M, Popli A, Pound MP, Pourreza R, Prasanna P, Prkovska V, Pridmore TP, Puch S, Puybareau \u00c9, Qian B, Qiao X, Rajchl M, Rane S, Rebsamen M, Ren H, Ren X, Revanuru K, Rezaei M, Rippel O, Rivera LC, Robert C, Rosen B, Rueckert D, Safwan M, Salem M, Salvi J, Sanchez I, S\u00e1nchez I, Santos HM, Sartor E, Schellingerhout D, Scheufele K, Scott MR, Scussel AA, Sedlar S, Serrano-Rubio JP, Shah NJ, Shah N, Shaikh M, Shankar BU, Shboul Z, Shen H, Shen D, Shen L, Shen H, Shenoy V, Shi F, Shin HE, Shu H, Sima D, Sinclair M, Smedby O, Snyder JM, Soltaninejad M, Song G, Soni M, Stawiaski J, Subramanian S, Sun L, Sun R, Sun J, Sun K, Sun Y, Sun G, Sun S, Suter YR, Szilagyi L, Talbar S, Tao D, Tao D, Teng Z, Thakur S, Thakur MH, Tharakan S, Tiwari P, Tochon G, Tran T, Tsai YM, Tseng K-L, Tuan TA, Turlapov V, Tustison N, Vakalopoulou M, Valverde S, Vanguri R, Vasiliev E, Ventura J, Vera L, Vercauteren T, Verrastro CA, Vidyaratne L, Vilaplana V, Vivekanandan A, Wang G, Wang Q, Wang CJ, Wang W, Wang D, Wang R, Wang Y, Wang C, Wang G, Wen N, Wen X, Weninger L, Wick W, Wu S, Wu Q, Wu Y, Xia Y, Xu Y, Xu X, Xu P, Yang T-L, Yang X, Yang H-Y, Yang J, Yang H, Yang G, Yao H, Ye X, Yin C, Young-Moxon B, Yu J, Yue X, Zhang S, Zhang A, Zhang K, Zhang X, Zhang L, Zhang X, Zhang Y, Zhang L, Zhang J, Zhang X, Zhang T, Zhao S, Zhao Y, Zhao X, Zhao L, Zheng Y, Zhong L, Zhou C, Zhou X, Zhou F, Zhu H, Zhu J, Zhuge Y, Zong W., Kalpathy-Cramer J, Farahani K, Davatzikos C, van Leemput K, Menze B (2018) Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge. arXiv Prepr. arXiv1811.02629"},{"key":"2714_CR117","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going Deeper with Convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 1\u20139","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"2714_CR118","doi-asserted-by":"crossref","unstructured":"Juntu J, Sijbers J, Van Dyck D, Gielen J (2005) Bias Field Correction for MRI Images. In: Computer Recognition Systems. pp. 543\u2013551. Springer Berlin Heidelberg, Berlin","DOI":"10.1007\/3-540-32390-2_64"},{"key":"2714_CR119","doi-asserted-by":"crossref","unstructured":"Li C, Huang R, Ding Z, Gatenby JC, Metaxas DN, Gore JC (2011) A Level Set Method for Image Segmentation in the Presence of Intensity Inhomogeneities With Application to MRI. 20, 2007\u20132016","DOI":"10.1109\/TIP.2011.2146190"},{"key":"2714_CR120","doi-asserted-by":"publisher","first-page":"1562","DOI":"10.1109\/TMI.2018.2791721","volume":"37","author":"G Wang","year":"2018","unstructured":"Wang G, Li W, Zuluaga MA, Pratt R, Patel PA, Aertsen M, Doel T, David AL, Deprest J, Ourselin S, Vercauteren T (2018) Interactive Medical Image Segmentation Using Deep Learning with Image-Specific Fine Tuning. IEEE Trans Med Imaging 37:1562\u20131573. https:\/\/doi.org\/10.1109\/TMI.2018.2791721","journal-title":"IEEE Trans Med Imaging"},{"key":"2714_CR121","unstructured":"Romero M, Interian Y, Solberg T, Valdes G (2019) Training Deep Learning models with small datasets. Prepr. ArXiv. Dec"},{"key":"2714_CR122","doi-asserted-by":"publisher","first-page":"574","DOI":"10.1148\/radiol.2017162326","volume":"284","author":"P Lakhani","year":"2017","unstructured":"Lakhani P, Sundaram B (2017) Deep Learning at Chest Radiography Lakhani and Sundaram. Radiology 284:574\u2013582","journal-title":"Radiology"}],"container-title":["Medical &amp; Biological Engineering &amp; Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-022-02714-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-022-02714-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-022-02714-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,6]],"date-time":"2023-01-06T02:18:17Z","timestamp":1672971497000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-022-02714-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,16]]},"references-count":122,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2023,1]]}},"alternative-id":["2714"],"URL":"https:\/\/doi.org\/10.1007\/s11517-022-02714-w","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"type":"print","value":"0140-0118"},{"type":"electronic","value":"1741-0444"}],"subject":[],"published":{"date-parts":[[2022,11,16]]},"assertion":[{"value":"16 September 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 October 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 November 2022","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 interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}