{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T09:20:33Z","timestamp":1770888033252,"version":"3.50.1"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"22","license":[{"start":{"date-parts":[[2022,2,24]],"date-time":"2022-02-24T00:00:00Z","timestamp":1645660800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,2,24]],"date-time":"2022-02-24T00:00:00Z","timestamp":1645660800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Shenzhen International Cooperation Foundation","award":["GJHZ20180926165402083"],"award-info":[{"award-number":["GJHZ20180926165402083"]}]},{"DOI":"10.13039\/501100000274","name":"British Heart Foundation","doi-asserted-by":"crossref","award":["TG\/18\/5\/34111"],"award-info":[{"award-number":["TG\/18\/5\/34111"]}],"id":[{"id":"10.13039\/501100000274","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100000274","name":"British Heart Foundation","doi-asserted-by":"crossref","award":["PG\/16\/78\/32402"],"award-info":[{"award-number":["PG\/16\/78\/32402"]}],"id":[{"id":"10.13039\/501100000274","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Hangzhou Economic and Technological Development Area Strategical Grant","award":["Imperial Institute of Advanced Technology"],"award-info":[{"award-number":["Imperial Institute of Advanced Technology"]}]},{"DOI":"10.13039\/501100010767","name":"Innovative Medicines Initiative","doi-asserted-by":"publisher","award":["H2020-JTI-IMI2 101005122"],"award-info":[{"award-number":["H2020-JTI-IMI2 101005122"]}],"id":[{"id":"10.13039\/501100010767","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007601","name":"Horizon 2020","doi-asserted-by":"publisher","award":["H2020-SC1-FA-DTS-2019-1 952172"],"award-info":[{"award-number":["H2020-SC1-FA-DTS-2019-1 952172"]}],"id":[{"id":"10.13039\/501100007601","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014013","name":"UK Research and Innovation","doi-asserted-by":"crossref","award":["MR\/V023799\/1"],"award-info":[{"award-number":["MR\/V023799\/1"]}],"id":[{"id":"10.13039\/100014013","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100003086","name":"Eusko Jaurlaritza","doi-asserted-by":"publisher","award":["IT1294-19"],"award-info":[{"award-number":["IT1294-19"]}],"id":[{"id":"10.13039\/501100003086","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2023,8]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Based on CT and MRI images acquired from normal pressure hydrocephalus (NPH) patients, using machine learning methods, we aim to establish a multimodal and high-performance automatic ventricle segmentation method to achieve an efficient and accurate automatic measurement of the ventricular volume. First, we extract the brain CT and MRI images of 143 definite NPH patients. Second, we manually label the ventricular volume (VV) and intracranial volume (ICV). Then, we use the machine learning method to extract features and establish automatic ventricle segmentation model. Finally, we verify the reliability of the model and achieved automatic measurement of VV and ICV. In CT images, the Dice similarity coefficient (DSC), intraclass correlation coefficient (ICC), Pearson correlation, and Bland\u2013Altman analysis of the automatic and manual segmentation result of the VV were 0.95, 0.99, 0.99, and 4.2\u2009\u00b1\u20092.6, respectively. The results of ICV were 0.96, 0.99, 0.99, and 6.0\u2009\u00b1\u20093.8, respectively. The whole process takes 3.4\u2009\u00b1\u20090.3\u00a0s. In MRI images, the DSC, ICC, Pearson correlation, and Bland\u2013Altman analysis of the automatic and manual segmentation result of the VV were 0.94, 0.99, 0.99, and 2.0\u2009\u00b1\u20090.6, respectively. The results of ICV were 0.93, 0.99, 0.99, and 7.9\u2009\u00b1\u20093.8, respectively. The whole process took 1.9\u2009\u00b1\u20090.1\u00a0s. We have established a multimodal and high-performance automatic ventricle segmentation method to achieve efficient and accurate automatic measurement of the ventricular volume of NPH patients. This can help clinicians quickly and accurately understand the situation of NPH patient's ventricles.<\/jats:p>","DOI":"10.1007\/s00521-022-07048-0","type":"journal-article","created":{"date-parts":[[2022,2,24]],"date-time":"2022-02-24T09:03:04Z","timestamp":1645693384000},"page":"16011-16020","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["AI-based medical e-diagnosis for fast and automatic ventricular volume measurement in patients with normal pressure hydrocephalus"],"prefix":"10.1007","volume":"35","author":[{"given":"Xi","family":"Zhou","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qinghao","family":"Ye","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaolin","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiakun","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haiqin","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Xia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Javier","family":"Del Ser","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7344-7733","authenticated-orcid":false,"given":"Guang","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,2,24]]},"reference":[{"key":"7048_CR1","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1056\/NEJM196507152730301","volume":"273","author":"RD Adams","year":"1965","unstructured":"Adams RD, Fisher CM, Hakim S, Ojemann RG, Sweet WH (1965) Symptomatic occult hydrocephalus with normal cerebrospinal-fluid pressure: a treatable syndrome. N Engl J Med 273:117\u2013126. https:\/\/doi.org\/10.1056\/NEJM196507152730301","journal-title":"N Engl J Med"},{"key":"7048_CR2","doi-asserted-by":"publisher","first-page":"63","DOI":"10.2176\/nmc.st.2020-0292","volume":"61","author":"M Nakajima","year":"2021","unstructured":"Nakajima M, Yamada S, Miyajima M (2021) Guidelines for Management of Idiopathic Normal Pressure Hydrocephalus (Third Edition): endorsed by the Japanese Society of Normal Pressure Hydrocephalus. Neurol Med Chir (Tokyo) 61:63\u201397. https:\/\/doi.org\/10.2176\/nmc.st.2020-0292","journal-title":"Neurol Med Chir (Tokyo)"},{"key":"7048_CR3","doi-asserted-by":"publisher","first-page":"661","DOI":"10.1007\/s00234-020-02361-8","volume":"62","author":"W He","year":"2020","unstructured":"He W, Fang X, Wang X (2020) A new index for assessing cerebral ventricular volume in idiopathic normal-pressure hydrocephalus: a comparison with Evans\u2019 index. Neuroradiology 62:661\u2013667. https:\/\/doi.org\/10.1007\/s00234-020-02361-8","journal-title":"Neuroradiology"},{"key":"7048_CR4","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/j.jalz.2012.11.007","volume":"9","author":"M Prince","year":"2013","unstructured":"Prince M, Bryce R, Albanese E, Wimo A, Ribeiro W, Ferri CP (2013) The global prevalence of dementia: a systematic review and metaanalysis. Alzheimers Dement 9:63\u201375. https:\/\/doi.org\/10.1016\/j.jalz.2012.11.007","journal-title":"Alzheimers Dement"},{"key":"7048_CR5","doi-asserted-by":"publisher","first-page":"1449","DOI":"10.1212\/WNL.0000000000000342","volume":"82","author":"D Jaraj","year":"2014","unstructured":"Jaraj D, Rabiei K, Marlow T, Jensen C, Skoog I, Wikkelso C (2014) Prevalence of idiopathic normal-pressure hydrocephalus. Neurology 82:1449\u20131454. https:\/\/doi.org\/10.1212\/WNL.0000000000000342","journal-title":"Neurology"},{"key":"7048_CR6","doi-asserted-by":"publisher","first-page":"585","DOI":"10.1016\/S1474-4422(15)00046-0","volume":"14","author":"H Kazui","year":"2015","unstructured":"Kazui H, Miyajima M, Mori E, Ishikawa M (2015) Lumboperitoneal shunt surgery for idiopathic normal pressure hydrocephalus (SINPHONI-2): an open-label randomised trial. Lancet Neurol 14:585\u2013594. https:\/\/doi.org\/10.1016\/S1474-4422(15)00046-0","journal-title":"Lancet Neurol"},{"key":"7048_CR7","doi-asserted-by":"publisher","first-page":"806","DOI":"10.1136\/jnnp-2013-306117","volume":"85","author":"K Andren","year":"2014","unstructured":"Andren K, Wikkelso C, Tisell M, Hellstrom P (2014) Natural course of idiopathic normal pressure hydrocephalus. J Neurol Neurosurg Psychiatry 85:806\u2013810. https:\/\/doi.org\/10.1136\/jnnp-2013-306117","journal-title":"J Neurol Neurosurg Psychiatry"},{"key":"7048_CR8","doi-asserted-by":"publisher","first-page":"850","DOI":"10.1016\/j.jalz.2017.01.013","volume":"13","author":"D Jaraj","year":"2017","unstructured":"Jaraj D, Wikkelso C, Rabiei K (2017) Mortality and risk of dementia in normal-pressure hydrocephalus: a population study. Alzheimers Dement 13:850\u2013857. https:\/\/doi.org\/10.1016\/j.jalz.2017.01.013","journal-title":"Alzheimers Dement"},{"key":"7048_CR9","doi-asserted-by":"publisher","first-page":"485","DOI":"10.1002\/ana.21739","volume":"66","author":"WM Palm","year":"2009","unstructured":"Palm WM, Saczynski JS, van der Grond J (2009) Ventricular dilation: association with gait and cognition. Ann Neurol 66:485\u2013493. https:\/\/doi.org\/10.1002\/ana.21739","journal-title":"Ann Neurol"},{"key":"7048_CR10","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1016\/j.jchemneu.2019.04.005","volume":"98","author":"H Kocaman","year":"2019","unstructured":"Kocaman H, Acer N, K\u00f6seo\u011flu E, G\u00fcltekin M, D\u00f6nmez H (2019) Evaluation of intracerebral ventricles volume of patients with Parkinson\u2019s disease using the atlas-based method: A methodological study. J Chem Neuroanaty 98:124\u2013130. https:\/\/doi.org\/10.1016\/j.jchemneu.2019.04.005","journal-title":"J Chem Neuroanaty"},{"key":"7048_CR11","doi-asserted-by":"publisher","first-page":"1051","DOI":"10.1016\/j.neuroimage.2011.06.080","volume":"58","author":"MJ Kempton","year":"2011","unstructured":"Kempton MJ, Underwood TSA, Brunton S (2011) A comprehensive testing protocol for MRI neuroanatomical segmentation techniques: evaluation of a novel lateral ventricle segmentation method. Neuroimage 58:1051\u20131059. https:\/\/doi.org\/10.1016\/j.neuroimage.2011.06.080","journal-title":"Neuroimage"},{"key":"7048_CR12","doi-asserted-by":"publisher","first-page":"131","DOI":"10.3171\/2020.6.PEDS20251","volume":"27","author":"JL Quon","year":"2021","unstructured":"Quon JL, Han M, Kim LH (2021) Artificial intelligence for automatic cerebral ventricle segmentation and volume calculation: a clinical tool for the evaluation of pediatric hydrocephalus. J Neurosurg Pediatrics 27:131\u2013138. https:\/\/doi.org\/10.3171\/2020.6.PEDS20251","journal-title":"J Neurosurg Pediatrics"},{"key":"7048_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101698","volume":"63","author":"F Dubost","year":"2020","unstructured":"Dubost F, Bruijne MD, Nardin M (2020) Multi-atlas image registration of clinical data with automated quality assessment using ventricle segmentation. Med Image Anal 63:101698. https:\/\/doi.org\/10.1016\/j.media.2020.101698","journal-title":"Med Image Anal"},{"key":"7048_CR14","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1016\/j.neuroimage.2015.05.099","volume":"118","author":"W Qiu","year":"2015","unstructured":"Qiu W, Yuan J, Rajchl M (2015) 3D MR ventricle segmentation in pre-term infants with post-hemorrhagic ventricle dilatation (PHVD) using multi-phase geodesic level-sets. Neuroimage 118:13\u201325. https:\/\/doi.org\/10.1016\/j.neuroimage.2015.05.099","journal-title":"Neuroimage"},{"key":"7048_CR15","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1007\/s12021-011-9135-9","volume":"10","author":"LE Poh","year":"2012","unstructured":"Poh LE, Gupta V, Johnson A, Kazmierski R, Nowinski WL (2012) Automatic segmentation of ventricular cerebrospinal fluid from ischemic stroke CT images. Neuroinformatics 10:159\u2013172. https:\/\/doi.org\/10.1007\/s12021-011-9135-9","journal-title":"Neuroinformatics"},{"key":"7048_CR16","doi-asserted-by":"publisher","first-page":"1871","DOI":"10.1109\/TBME.2017.2783305","volume":"65","author":"V Cherukuri","year":"2018","unstructured":"Cherukuri V, Ssenyonga P, Warf BC, Kulkarni AV, Monga V, Schiff SJ (2018) Learning based segmentation of CT brain images: application to postoperative hydrocephalic scans. IEEE Trans Biomed Eng 65:1871\u20131884. https:\/\/doi.org\/10.1109\/TBME.2017.2783305","journal-title":"IEEE Trans Biomed Eng"},{"key":"7048_CR17","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1227\/01.NEU.0000370939.30003.D1","volume":"67","author":"K Ambarki","year":"2010","unstructured":"Ambarki K, Israelsson H, W\u00e5hlin A, Birgander R, Eklund A, Malm J (2010) Brain ventricular size in healthy elderly. Neurosurgery 67:94\u201399. https:\/\/doi.org\/10.1227\/01.NEU.0000370939.30003.D1","journal-title":"Neurosurgery"},{"key":"7048_CR18","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1016\/j.inffus.2018.10.009","volume":"51","author":"MM Hassan","year":"2019","unstructured":"Hassan MM, Alam MGR, Uddin MZ, Huda S, Almogren A, Fortino G (2019) Human emotion recognition using deep belief network architecture. Inform Fusion 51:10\u201318. https:\/\/doi.org\/10.1016\/j.inffus.2018.10.009","journal-title":"Inform Fusion"},{"key":"7048_CR19","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1016\/j.jnca.2018.05.007","volume":"117","author":"Y Zhang","year":"2018","unstructured":"Zhang Y, Gravina R, Lu H, Villari M, Fortino G (2018) PEA: Parallel electrocardiogram-based authentication for smart healthcare systems. J Netw Computer Appl 117:10\u201316. https:\/\/doi.org\/10.1016\/j.jnca.2018.05.007","journal-title":"J Netw Computer Appl"},{"key":"7048_CR20","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1016\/j.inffus.2020.09.006","volume":"66","author":"F Piccialli","year":"2021","unstructured":"Piccialli F, Somma VD, Giampaolo F, Cuomo S, Fortino G (2021) A survey on deep learning in medicine: Why, how and when? Inform Fusion 66:113\u2013137. https:\/\/doi.org\/10.1016\/j.inffus.2020.09.006","journal-title":"Inform Fusion"},{"key":"7048_CR21","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1016\/j.inffus.2021.07.016","volume":"77","author":"G Yang","year":"2022","unstructured":"Yang G, Ye Q, Xia J (2022) Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: A mini-review, two showcases and beyond. Inform Fusion 77:29\u201352. https:\/\/doi.org\/10.1016\/j.inffus.2021.07.016","journal-title":"Inform Fusion"},{"key":"7048_CR22","doi-asserted-by":"publisher","DOI":"10.1007\/s12021-021-09510-1","author":"EE Ntiri","year":"2021","unstructured":"Ntiri EE, Holmes MF, Forooshani PM (2021) Improved segmentation of the intracranial and ventricular volumes in populations with cerebrovascular lesions and atrophy using 3D CNNs. Neuroinformatics. https:\/\/doi.org\/10.1007\/s12021-021-09510-1","journal-title":"Neuroinformatics"},{"key":"7048_CR23","doi-asserted-by":"publisher","first-page":"1923","DOI":"10.1007\/s11548-019-02038-5","volume":"14","author":"TJ Huff","year":"2019","unstructured":"Huff TJ, Ludwig PE, Salazar D, Cramer JA (2019) Fully automated intracranial ventricle segmentation on CT with 2D regional convolutional neural network to estimate ventricular volume. Int J Comp Assisted Radiol Surg 14:1923\u20131932. https:\/\/doi.org\/10.1007\/s11548-019-02038-5","journal-title":"Int J Comp Assisted Radiol Surg"},{"key":"7048_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.nicl.2019.101871","volume":"23","author":"M Shao","year":"2019","unstructured":"Shao M, Han S, Carass A (2019) Brain ventricle parcellation using a deep neural network: Application to patients with ventriculomegaly. Neuroimage Clin 23:101871. https:\/\/doi.org\/10.1016\/j.nicl.2019.101871","journal-title":"Neuroimage Clin"},{"key":"7048_CR25","doi-asserted-by":"publisher","first-page":"1571","DOI":"10.1109\/JBHI.2017.2776246","volume":"22","author":"S Zhao","year":"2017","unstructured":"Zhao S, Gao Z, Zhang H et al (2017) Robust segmentation of intima-media borders with different morphologies and dynamics during the cardiac cycle. IEEE J Biomed Health Informatics 22:1571\u20131582. https:\/\/doi.org\/10.1109\/JBHI.2017.2776246","journal-title":"IEEE J Biomed Health Informatics"},{"key":"7048_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101826","volume":"67","author":"S Zhao","year":"2021","unstructured":"Zhao S, Wu X, Chen B, Li S (2021) Automatic vertebrae recognition from arbitrary spine MRI images by a category-Consistent self-calibration detection framework. Med Image Anal 67:101826. https:\/\/doi.org\/10.1016\/j.media.2020.101826","journal-title":"Med Image Anal"},{"key":"7048_CR27","doi-asserted-by":"publisher","first-page":"1187","DOI":"10.3174\/ajnr.A6620","volume":"41","author":"J Neikter","year":"2020","unstructured":"Neikter J, Agerskov S, Hellstr\u00f6m P (2020) Ventricular volume is more strongly associated with clinical improvement than the evans index after shunting in idiopathic normal pressure hydrocephalus. Am J Neuroradiol 41:1187\u20131192. https:\/\/doi.org\/10.3174\/ajnr.A6620","journal-title":"Am J Neuroradiol"},{"key":"7048_CR28","doi-asserted-by":"publisher","unstructured":"Mori E, Ishikawa M, Kato T (2012) Guidelines for management of idiopathic normal pressure hydrocephalus: second edition. Neurol Med Chir (Tokyo) 52:775\u2013809. https:\/\/doi.org\/10.2176\/nmc.52.775","DOI":"10.2176\/nmc.52.775"},{"key":"7048_CR29","doi-asserted-by":"publisher","DOI":"10.3389\/fnagi.2020.618538","volume":"12","author":"X Zhou","year":"2020","unstructured":"Zhou X, Ye Q, Jiang Y (2020) Systematic and comprehensive automated ventricle segmentation on ventricle images of the elderly patients: a retrospective study. Front Aging Neurosci 12:618538. https:\/\/doi.org\/10.3389\/fnagi.2020.618538","journal-title":"Front Aging Neurosci"},{"key":"7048_CR30","doi-asserted-by":"publisher","first-page":"266","DOI":"10.1002\/jmri.25927","volume":"48","author":"SX Zhao","year":"2018","unstructured":"Zhao SX, Xiao YH, Lv FR, Zhang ZW, Sheng B, Ma HL (2018) Lateral ventricular volume measurement by 3D MR hydrography in fetal ventriculomegaly and normal lateral ventricles. J Magnetic Resonance Imaging 48:266\u2013273. https:\/\/doi.org\/10.1002\/jmri.25927","journal-title":"J Magnetic Resonance Imaging"},{"key":"7048_CR31","doi-asserted-by":"publisher","first-page":"S4","DOI":"10.1186\/1472-6947-9-S1-S4","volume":"9","author":"W Chen","year":"2009","unstructured":"Chen W, Smith R, Ji S, Ward KR, Najarian K (2009) Automated ventricular systems segmentation in brain CT images by combining low-level segmentation and high-level template matching. BMC Med Inform Decis Making 9:S4. https:\/\/doi.org\/10.1186\/1472-6947-9-S1-S4","journal-title":"BMC Med Inform Decis Making"},{"key":"7048_CR32","doi-asserted-by":"publisher","first-page":"615","DOI":"10.1016\/j.neuroimage.2007.11.047","volume":"40","author":"Y Chou","year":"2008","unstructured":"Chou Y, Lepor\u00e9 N, de Zubicaray GI (2008) Automated ventricular mapping with multi-atlas fluid image alignment reveals genetic effects in Alzheimer\u2019s disease. Neuroimage 40:615\u2013630. https:\/\/doi.org\/10.1016\/j.neuroimage.2007.11.047","journal-title":"Neuroimage"},{"key":"7048_CR33","doi-asserted-by":"publisher","first-page":"61","DOI":"10.3389\/fnins.2015.00061","volume":"9","author":"X Tang","year":"2015","unstructured":"Tang X, Crocetti D, Kutten K (2015) Segmentation of brain magnetic resonance images based on multi-atlas likelihood fusion: testing using data with a broad range of anatomical and photometric profiles. Front Neurosci 9:61. https:\/\/doi.org\/10.3389\/fnins.2015.00061","journal-title":"Front Neurosci"},{"key":"7048_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2017\/8690892","volume":"2017","author":"X Qian","year":"2017","unstructured":"Qian X, Lin Y, Zhao Y, Yue X, Lu B, Wang J (2017) Objective Ventricle segmentation in brain ct with ischemic stroke based on anatomical knowledge. Biomed Res Int 2017:1\u201311. https:\/\/doi.org\/10.1155\/2017\/8690892","journal-title":"Biomed Res Int"},{"key":"7048_CR35","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1016\/j.jalz.2011.01.003","volume":"7","author":"A Tarnaris","year":"2011","unstructured":"Tarnaris A, Toma AK, Pullen E (2011) Cognitive, biochemical, and imaging profile of patients suffering from idiopathic normal pressure hydrocephalus. Alzheimers Dement 7:501\u2013508. https:\/\/doi.org\/10.1016\/j.jalz.2011.01.003","journal-title":"Alzheimers Dement"},{"key":"7048_CR36","first-page":"8300","volume":"2021","author":"D Li","year":"2021","unstructured":"Li D, Yang J, Kreis K, Torralba A, Fidler S (2021) Semantic segmentation with generative models: Semi-supervised learning and strong out-of-domain generalization. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition 2021:8300\u20138311","journal-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition"},{"key":"7048_CR37","doi-asserted-by":"publisher","DOI":"10.3389\/fneur.2021.636518","volume":"12","author":"H Zhang","year":"2021","unstructured":"Zhang H, He WJ, Liang LH (2021) Diffusion spectrum imaging of corticospinal tracts in idiopathic normal pressure hydrocephalus. Front Neurol 12:636518. https:\/\/doi.org\/10.3389\/fneur.2021.636518","journal-title":"Front Neurol"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07048-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-022-07048-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07048-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,12]],"date-time":"2023-07-12T20:48:49Z","timestamp":1689194929000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-022-07048-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,24]]},"references-count":37,"journal-issue":{"issue":"22","published-print":{"date-parts":[[2023,8]]}},"alternative-id":["7048"],"URL":"https:\/\/doi.org\/10.1007\/s00521-022-07048-0","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,24]]},"assertion":[{"value":"11 August 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 January 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 February 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":"There are no potential competing interests in our paper. And all authors have seen the manuscript and approved to submit to your journal. We confirm that the content of the manuscript has not been published or submitted for publication elsewhere.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This study passed the ethical approval of The First Affiliated Hospital of Shenzhen University's bioethics committee (approval no. KS20190114001), and the researchers all signed the informed consent form.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"Informed consent was obtained from all individual participants included in the study.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"All authors have read and agreed to the published version of the manuscript.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}]}}