AI-based medical e-diagnosis for fast and automatic ventricular volume measurement in patients with normal pressure hydrocephalus
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2022-02-24
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Abstract
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–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.
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This study is supported in part by Project of Shenzhen International Cooperation Foundation (GJHZ20180926165402083),_x000D_ in part by the National Natural Science Foundation of China (grant number 82171913), _x000D_ in part by the funding support from the Basque Government (Eusko Jaurlaritza) through the Consolidated Research_x000D_ Group MATHMODE (IT1294-19),_x000D_ in part by the British Heart Foundation (Project Number: TG/18/5/34111, PG/16/78/32402), _x000D_ in part by the Hangzhou Economic and Technological Development Area Strategical Grant (Imperial Institute of Advanced Technology),_x000D_ 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],_x000D_ 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],_x000D_ in part by the MRC (MC/PC/21013), _x000D_ and in part by the UK Research and Innovation Future Leaders Fellowship [MR/V023799/1]
Keywords
Normal pressure hydrocephalus , Machine learning , Computed tomography , Magnetic resonance imaging , Ventricular volume , Intracranial volume , Medical AI , Normal pressure hydrocephalus , Machine learning , Computed tomography , Magnetic resonance imaging , Ventricular volume , Intracranial volume , Medical AI , AI-based diagnosis , Software , Artificial Intelligence , SDG 3 - Good Health and Well-being , Project ID , info:eu-repo/grantAgreement/EC/H2020/101005122/EU/rapiD and secuRe AI imaging based diaGnosis, stratification, fOllow-up, and preparedness for coronavirus paNdemics/DRAGON , info:eu-repo/grantAgreement/EC/H2020/952172/EU/Accelerating the Lab to Market Transition of AI Tools for Cancer Management/CHAIMELEON , info:eu-repo/grantAgreement/EC/H2020/101005122/EU/rapiD and secuRe AI imaging based diaGnosis, stratification, fOllow-up, and preparedness for coronavirus paNdemics/DRAGON , info:eu-repo/grantAgreement/EC/H2020/952172/EU/Accelerating the Lab to Market Transition of AI Tools for Cancer Management/CHAIMELEON , Funding Info , This study is supported in part by Project of Shenzhen International Cooperation Foundation (GJHZ20180926165402083),_x000D_ in part by the National Natural Science Foundation of China (grant number 82171913), _x000D_ in part by the funding support from the Basque Government (Eusko Jaurlaritza) through the Consolidated Research_x000D_ Group MATHMODE (IT1294-19),_x000D_ in part by the British Heart Foundation (Project Number: TG/18/5/34111, PG/16/78/32402), _x000D_ in part by the Hangzhou Economic and Technological Development Area Strategical Grant (Imperial Institute of Advanced Technology),_x000D_ 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],_x000D_ in part by the AI for Health Imaging Award ‘‘CHAIMELEON: Accelerating t , This study is supported in part by Project of Shenzhen International Cooperation Foundation (GJHZ20180926165402083),_x000D_ in part by the National Natural Science Foundation of China (grant number 82171913), _x000D_ in part by the funding support from the Basque Government (Eusko Jaurlaritza) through the Consolidated Research_x000D_ Group MATHMODE (IT1294-19),_x000D_ in part by the British Heart Foundation (Project Number: TG/18/5/34111, PG/16/78/32402), _x000D_ in part by the Hangzhou Economic and Technological Development Area Strategical Grant (Imperial Institute of Advanced Technology),_x000D_ 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],_x000D_ in part by the AI for Health Imaging Award ‘‘CHAIMELEON: Accelerating t
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Zhou , X , Ye , Q , Yang , X , Chen , J , Ma , H , Xia , J , Del Ser , J & Yang , G 2022 , ' AI-based medical e-diagnosis for fast and automatic ventricular volume measurement in patients with normal pressure hydrocephalus ' , Neural Computing and Applications , vol. unknown , no. 22 , pp. 16011-16020 . https://doi.org/10.1007/s00521-022-07048-0