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dc.contributor.authorZhou, Xi
dc.contributor.authorYe, Qinghao
dc.contributor.authorYang, Xiaolin
dc.contributor.authorChen, Jiakun
dc.contributor.authorMa, Haiqin
dc.contributor.authorXia, Jun
dc.contributor.authorDel Ser, Javier
dc.contributor.authorYang, Guang
dc.date.accessioned2022-03-20T16:48:17Z
dc.date.available2022-03-20T16:48:17Z
dc.date.issued2022-02-24
dc.identifier.citationZhou, Xi, Qinghao Ye, Xiaolin Yang, Jiakun Chen, Haiqin Ma, Jun Xia, Javier Del Ser, and Guang Yang. “AI-Based Medical e-Diagnosis for Fast and Automatic Ventricular Volume Measurement in Patients with Normal Pressure Hydrocephalus.” Neural Computing and Applications (February 24, 2022). doi:10.1007/s00521-022-07048-0.en
dc.identifier.issn0941-0643en
dc.identifier.urihttp://hdl.handle.net/11556/1288
dc.description.abstractBased 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–Altman analysis of the automatic and manual segmentation result of the VV were 0.95, 0.99, 0.99, and 4.2 ± 2.6, respectively. The results of ICV were 0.96, 0.99, 0.99, and 6.0 ± 3.8, respectively. The whole process takes 3.4 ± 0.3 s. In MRI images, the DSC, ICC, Pearson correlation, and Bland–Altman analysis of the automatic and manual segmentation result of the VV were 0.94, 0.99, 0.99, and 2.0 ± 0.6, respectively. The results of ICV were 0.93, 0.99, 0.99, and 7.9 ± 3.8, respectively. The whole process took 1.9 ± 0.1 s. 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.en
dc.description.sponsorshipThis study is supported in part by Project of Shenzhen International Cooperation Foundation (GJHZ20180926165402083), in part by the National Natural Science Foundation of China (grant number 82171913), in part by the funding support from the Basque Government (Eusko Jaurlaritza) through the Consolidated Research Group MATHMODE (IT1294-19), in part by the British Heart Foundation (Project Number: TG/18/5/34111, PG/16/78/32402), in part by the Hangzhou Economic and Technological Development Area Strategical Grant (Imperial Institute of Advanced Technology), in part by the European Research Council Innovative Medicines Initiative on Development of Therapeutics and Diagnostics Combat ting Coronavirus Infections Award ‘‘DRAGON: rapiD and secuRe AI imaging based diaGnosis, stratification, fOllow-up, and preparedness for coronavirus paNdemics’’ [H2020-JTI-IMI2 101005122], in part by the AI for Health Imaging Award ‘‘CHAIMELEON: Accelerating the Lab to Market Transition of AI Tools for Cancer Management’’ [H2020-SC1-FA-DTS-2019-1 952172], in part by the MRC (MC/PC/21013), and in part by the UK Research and Innovation Future Leaders Fellowship [MR/V023799/1]en
dc.language.isoengen
dc.publisherSpringer Science and Business Media Deutschland GmbHen
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.titleAI-based medical e-diagnosis for fast and automatic ventricular volume measurement in patients with normal pressure hydrocephalusen
dc.typearticleen
dc.identifier.doi10.1007/s00521-022-07048-0en
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/101005122/EU/rapiD and secuRe AI imaging based diaGnosis, stratification, fOllow-up, and preparedness for coronavirus paNdemics/DRAGONen
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/952172/EU/Accelerating the Lab to Market Transition of AI Tools for Cancer Management/CHAIMELEONen
dc.rights.accessRightsopenAccessen
dc.subject.keywordsNormal pressure hydrocephalusen
dc.subject.keywordsMachine learningen
dc.subject.keywordsComputed tomographyen
dc.subject.keywordsMagnetic resonance imagingen
dc.subject.keywordsVentricular volumeen
dc.subject.keywordsIntracranial volumeen
dc.subject.keywordsMedical AIen
dc.identifier.essn1433-3058en
dc.journal.titleNeural Computing and Applicationsen


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