基于磁共振图像的帕金森病计算机辅助诊断研究进展

doi:10.3969/j.issn.0258-8021.2020.05.011

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Abstract In recent years,due to the clinical complexity of Parkinson’s disease (PD) and the high-dimensional nature of multi-modemagnetic resonance (MR) images,how to effectively use the specific image biomarkers and establish an efficient Computer-aided Diagnosis (CAD) model for disease diagnosis is a challenging problem in PD research. This paper reviewed the research progress,and summarized key techniques of CAD modeling based on traditional machine learning methods such as feature extraction,feature selection andthe classifier model. This paper also briefly introduced the recent research and application of deep learning in early PD classification diagnosis. It is pointed out that based on multi-modal images,CAD model constructed by machine learning or deep learning can recognize PD patients and normal people objectively and accurately,which has great value and application prospect to improve the accuracy of early PD diagnosis. Future researches should be carried out to explore the potential biomarkers of PD in multi-modality images,and to develop higher-order CAD models to assist the clinical intelligent diagnosis of early PD.

Key words： Parkinson′s disease (PD) magnetic resonance imaging (MRI) computer-aided diagnosis (CAD) machine learning deep learning

Received: 18 November 2019

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R318

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	Yang Yifeng
	Hu Ying
	Nie Shengdong

Cite this article:

Yang Yifeng,Hu Ying,Nie Shengdong. Progress in Computer-Aided Diagnosis of Parkinson′s Disease Based on Magnetic Resonance Imaging[J]. Chinese Journal of Biomedical Engineering, 2020, 39(5): 603-610.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2020.05.011 OR http://cjbme.csbme.org/EN/Y2020/V39/I5/603

[1] Cummings JL,Henchcliffe C,Schaier S,et al.The role of dopaminergic imaging in patients with symptoms of dopaminergic system neurodegeneration[J].Brain,2011,134(11):3146-3166.
[2] Strafella AP,Bohnen NI,Pavese N,et al.Imaging markers of progression in Parkinson′s disease[J].Movement Disorders Clinical Practice,2018,5(6):586-596.
[3] Sajda P.Machine learning for detection and diagnosis of disease[J].Annual Review of Biomedical Engineering,2006,8:537-565.
[4] Rathore S,Habes M,Iftikhar MA,et al.A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer′s disease and its prodromal stages[J].NeuroImage,2017,155:530-548.
[5] Yang Xueyu,Chen Kewei,Guo Xiaojuan,et al.Validation of voxel-based morphometry (VBM) based on MRI[C]//Medical Imaging 2007:Image Processing.International Society for Optics and Photonics,2007,6512:65121G.
[6] Li Xingfeng,Xing Yue,Martin-Bastida A,et al.Patterns of grey matter loss associated with motor subscores in early Parkinson′s disease[J].NeuroImage:Clinical,2018,17:498-504.
[7] Schipper LJ,van der Grond J,Marinus J,et al.Loss of integrity and atrophy in cingulate structural covariance networks in Parkinson′s disease[J].NeuroImage:Clinical,2017,15:587-593.
[8] Oosterwijk CS,Vriend C,Berendse HW,et al.Anxiety in Parkinson′s disease is associated with reduced structural covariance of the striatum[J].Journal of Affective Disorders,2018,240:113-120.
[9] Babu GS,Suresh S,Mahanand BS.A novel PBL-McRBFN-RFE approach for identification of critical brain regions responsible for Parkinson′s disease[J].Expert Systems with Applications,2014,41(2):478-488.
[10] Adeli E,Shi F,An L,et al.Joint feature-sample selection and robust diagnosis of Parkinson′s disease from MRI data[J].NeuroImage,2016,141:206-219.
[11] Cigdem O,Beheshti I,Demirel H.Effects of different covariates and contrasts on classification of Parkinson′s disease using structural MRI[J].Computers in Biology and Medicine,2018,99:173-181.
[12] Rana B,Juneja A,Saxena M,et al.Regions-of-interest based automated diagnosis of Parkinson’s disease using T1-weighted MRI[J].Expert Systems with Applications,2015,42(9):4506-4516.
[13] Amoroso N,La Rocca M,Monaco A,et al.Complex networks reveal early MRI markers of Parkinson′s disease[J].Medical Image Analysis,2018,48:12-24.
[14] Jellinger KA.Neuropathology of sporadic Parkinson′s disease:evaluation and changes of concepts[J].Movement Disorders,2012,27(1):8-30.
[15] Vaillancourt DE,Spraker MB,Prodoehl J,et al.High-resolution diffusion tensor imaging in the substantia nigra of de novo Parkinson disease[J].Neurology,2009,72(16):1378-1384.
[16] Mahlknecht P,Krismer F,Poewe W,et al.Meta‐analysis of dorsolateral nigral hyperintensity on magnetic resonance imaging as a marker for Parkinson′s disease[J].Movement Disorders,2017,32(4):619-623.
[17] Tambasco N,Paoletti FP,Chiappiniello A,et al.T2*-weighted MRI values correlate with motor and cognitive dysfunction in Parkinson′s disease[J].Neurobiology of Aging,2019,80:91-98.
[18] Pyatigorskaya N,Magnin B,Mongin M,et al.Comparative study of MRI biomarkers in the substantia nigra to discriminate idiopathic Parkinson disease[J].American Journal of Neuroradiology,2018,39(8):1460-1467.
[19] Ryman SG,Poston KL.MRI biomarkers of motor and non-motor symptoms in Parkinson′s disease[J].Parkinsonism &Related Disorders,10 Oct,2019[Epub ahead of print]
[20] Cheng Zhenghui,He Naying,Huang Pei,Imaging the Nigrosome 1 in the substantia nigra using susceptibility weighted imaging and quantitative susceptibility mapping:An application to Parkinson′s disease[J].NeuroImage:Clinical,2020,25:102103.
[21] Li Gaiying,Zhai Guoqianf,Zhao Xinxin.3D texture analyses within the substantia nigra of Parkinson′s disease patients on quantitative susceptibility maps and R2 * maps[J].Neuroimage,2019,188:465-472.
[22] Trufanov AG,Yurin AA,Buriak AB,et al.Susceptibility-weighted MR imaging (SWI) of basal ganglia iron deposition in the early and advanced stages of Parkinson′s disease[J].Neurology,Neuropsychiatry,Psychosomatics,2019,11(2):30-36.
[23] Chen Yongbin,Yang Wanqun,Long Jinyi,et al.Discriminative analysis of Parkinson’s disease based on whole-brain functional connectivity[J].PLoS ONE,2015,10(4):e0124153.
[24] Filippi M,Basaia S,Zahedmanesh H,et al.Progression of Parkinson’s disease:A longitudinal MRI study of functional brain connectome in a large cohort of patients (P5.8-010)[J].Neurology,9 May,2019[Epub ahead of print].
[25] Tang Yan,Meng Li,Wan Changmin,et al.Identifying the presence of Parkinson’s disease using low-frequency fluctuations in BOLD signals[J].Neuroscience Letters,2017,645:1-6.
[26] Rubbert C,Hoffstaedter F,Schnitzler A,et al.Machine-learning identifies Parkinson′s disease patients based on resting-state between-network functional connectivity[J].The British Journal of Radiology,2019,92:20180886.
[27] Abós A,Baggio HC,Segura B,et al.Discriminating cognitive status in Parkinson’s disease through functional connectomics and machine learning[J].Scientific Reports,2017,7:45347.
[28] Gu Quanquan,Zhang Huan,Xuan Min,et al.Automatic classification on multi-modal MRI data for diagnosis of the postural instability and gait difficulty subtype of Parkinson’s disease[J].Journal of Parkinson′s Disease,2016,6(3):545-556.
[29] Bowman FDB,Drake DF,Huddleston DE.Multimodal imaging signatures of Parkinson′s disease[J].Frontiers in Neuroscience,2016,10:131.
[30] Singh G,Samavedham L.Algorithm for image-based biomarker detection for differential diagnosis of Parkinson′s disease[J].IFAC-PapersOnLine,2015,48(8):918-923.
[31] Péran P,Barbagallo G,Nemmi F,et al.MRI supervised and unsupervised classification of Parkinson′s disease and multiple system atrophy[J].Movement Disorders,2018,33(4):600-608.
[32] Prange S,Metereau E,Thobois S.Structural imaging in Parkinson’s disease:New developments[J].Current Neurology and Neuroscience Reports,2019,19(8):50.
[33] Wolpert DH,Macready WG.No free lunch theorems for optimization[J].IEEE Transactions on Evolutionary Computation,1997,1(1):67-82.
[34] Hafiz AM,Bhat GM.A survey of deep learning techniques for medical diagnosis[M]//Information and Communication Technology for Sustainable Development.Singapore City:Springer,2020:161-170.
[35] Akkus Z,Galimzianova A,Hoogi A,et al.Deep learning for brain MRI segmentation:state of the art and future directions[J].Journal of Digital Imaging,2017,30(4):449-459.
[36] Janghel RR.Deep-learning-based classification and diagnosis of Alzheimer′s ddisease[M]//Mehdi KP.Deep Learning and Neural Networks:Concepts,Methodologies,Tools,and Applications.Hershey:IGI Global,2020:1358-1382.
[37] Esmaeilzadeh S,Yang Yao,Adeli E.End-to-end Parkinson disease diagnosis using brain MR images by 3D-CNN[EB/OL].https://arxiv.org/abs/1806.05233,2018-06-13/2018-12-20.
[38] Saha R.Classification of Parkinson’s disease using MRI data and deep learning convolution neural networks[J].Creative Components,241,2019[Epub ahead of print].
[39] 张巧丽,迟学斌,赵地.基于深度学习的帕金森病症早期诊断[J].计算机系统应用,2018,27(9):1-9.
[40] Shinde S,Prasad S,Saboo Y,et al.Predictive markers for Parkinson′s disease using deep neural nets on neuromelanin sensitive MRI[J].NeuroImage:Clinical,2019,22:101748.
[41] Sivaranjini S,Sujatha CM.Deep learning based diagnosis of Parkinson′s disease using convolutional neural network[J].Multimedia Tools and Applications,2019(5):1-13.
[42] Pereira HR,Ferreira HA.Classification of patients with Parkinson′s disease using medical imaging and artificial intelligence algorithms[C]//Mediterranean Conference on Medical and Biological Engineering and Computing.Springer,Cham,2019:2043-2056.
[43] Xu Jiahang,Jiao Fangyang,Huang Yechong,et al.A fully-automatic framework for Parkinson′s disease diagnosis by multi-modality images[J].Frontiers in Neuroscience,2019,13:874.
[44] Cheng Zenghui,Zhang Jiping,He Naying,et al.Radiomic features of the nigrosome-1 region of the substantia nigra:Using quantitative susceptibility mapping to assist the diagnosis of idiopathic Parkinson’s disease[J].Frontiers in Aging Neuroscience,2019,11:167.
[45] Singh G,Vadera M,Samavedham L,et al.Multi-class diagnosis of Neurodegenerative diseases:an Neuroimaging machine learning based approach[J].Industrial &Engineering Chemistry Research,2019,58(26):11498-11505.
[46] Sakai K,Yamada K.Machine learning studies on major brain diseases:5-year trends of 2014-2018[J].Japanese Journal of Radiology,2019,37(1):34-72.
[47] Zeighami Y,Ulla M,Iturria-Medina Y,et al.Network structure of brain atrophy in de novo Parkinson′s disease[J].Elife,2015,4:e08440.
[48] Ariz M,Abad RC,Castellanos G,et al.Dynamic atlas-based segmentation and quantification of neuromelanin-rich brainstem structures in Parkinson disease[J].IEEE Transactions on Medical Imaging,2018,38(3):813-823.