计算机工程与科学 ›› 2022, Vol. 44 ›› Issue (05): 879-893.
楚阳,徐文龙
出版日期:
2022-05-25
发布日期:
2022-05-24
基金资助:
CHU Yang,XU Wen-long
Online:
2022-05-25
Published:
2022-05-24
摘要: 阿尔兹海默症(AD)作为主要的神经退行性疾病之一,已成为导致痴呆问题最常见的原因。截至目前,尚缺乏有效的针对性治疗药物和阻止疾病发展的有效治疗方式。随着计算机技术的不断发展,将计算机辅助诊断技术工具用于AD早期分类研究将为临床医生提供重要帮助。综述近些年来将传统机器学习和深度学习技术等手段用于AD的早期诊断分类的研究,研究样本主要为脑部神经成像数据(如MRI、PET)、脑电图(EEG)等生物标记物,结合机器学习方法对AD早期诊断进行分类研究。首先分析了将机器学习方法用于AD早期分类的应用,对比了采用不同算法的分类情况;其次,对比了针对受试者不同生物标记物以及采用单模态或不同模态组合方式用于AD早期分类的研究;最后介绍了AD分类面临的挑战并提出了未来的研究方向。
楚阳, 徐文龙. 基于计算机辅助诊断技术的阿尔兹海默症早期分类研究综述[J]. 计算机工程与科学, 2022, 44(05): 879-893.
CHU Yang, XU Wen-long. Review of early classification of Alzheimers disease based on computer-aided diagnosis technology[J]. Computer Engineering & Science, 2022, 44(05): 879-893.
[1] | AlzheimerS Association.2018 Alzheimers disease facts and figures[J].Alzheimers & Dementia,2019,15(3):321-387. |
[2] | Falahati F,Westman E,Simmons A.Multivariate data analysis and machine learning in Alzheimers disease with a focus on structural magnetic resonance imaging[J].Journal of Alzheimers Disease,2014,41(3):685-708. |
[3] | Mitchell A J,Shiri-Feshki M.Rate of progression of mild cognitive impairment to dementia-meta-analysis of 41 robust inception cohort studies[J].Acta Psychiatrica Scandinavica,2009,119(4):252-265. |
[4] | Dubois B,Feldman H H,Jacova C,et al.Research criteria for the diagnosis of Alzheimers disease:Revising the Nincds-Adrda criteria[J].Lancet Neurol,2007,6(8):734-746. |
[5] | LeCun Y,Bengio Y,Hinton G.Deep learning[J].Nature,2015,521(7553):436-444. |
[6] | Galton C J,Gomez-Anson B,Antoun N,et al.Temporal lobe rating scale:Application to Alzheimers disease and frontotemporal dementia[J].Journal of Neurology Neurosurgery Psychiatry,2001,70(2):165-173. |
[7] | Lerch J P,Pruessner J,Zijdenbos A P,et al.Automated cortical thic-kness measurements from MRI can accurately separate Alzheimers patients from normal elderly controls[J].Neurobiology of Aging,2008,29(1):23-30. |
[8] | Gerardin E,Chételat G,Chupin M,et al.Multidimensional classification of hippocampal shape features discriminates Alzheimers disease and mild cognitive impairment from normal aging[J].NeuroImage,2009,47(4):1476-1486. |
[9] | Freeborough P A,Fox N C.MR image texture analysis applied to the diagnosis and tracking of Alzheimers disease[J].IEEE Transactions on Medical Imaging,1998,17(3):475-478. |
[10] | Karas G B,Scheltens P,Rombouts S A R B,et al.Global and local gray matter loss in mild cognitive impairment and Alzheimers disease[J].NeuroImage,2004,23(2):708-716. |
[11] | Liu M H, Li F,Yan H,et al.A multi-model deep convoluti-onal neural network for automatic hippocampus segmentation and classification in Alzheimer’s disease[J].NeuroImage,2020,208:116459. |
[12] | Amoroso N, la Rocca M, Bellotti R, et al. Alzheimers disease diagnosis based on the Hippocampal unified multi- Atlas network(HU-MAN) algorithm[J].Biomedical Engineering Online,2018,17(1):No.6. |
[13] | Kim H W, Lee H E, Lee S W,et al.Slice-selective learning for Alzheimers disease classification using a generative adversarial network:A feasibility study of external validation[J].European Journal of Nuclear Medicine and Molecular Imaging,2020,47:2197-2206. |
[14] | Ieracitano C,Mammone N,Hussain A,et al.A novel multi-modal machine learning based approach for automatic classification of EEG recordings in dementia[J].Neural Networks,2020,123:176-190. |
[15] | Ahmad F,Zulifqar H,Malik T.Classification of Alzheimer disease among susceptible brain regions[J].International Journal of Imaging Systems and Technology,2019,29(3):222-233. |
[16] | Gupta Y, Lee K H, Choi K Y,et al.Alzheimers disease diagnosis based on cortical and subcortical features[J].Journal of Healthcare Engineering,2019,2019:2492719. |
[17] | Dimitriadis S I, Liparas D, Tsolaki M N.Random forest feature selection,fusion and ensemble strategy:Combining multiple morphological MRI measures to discriminate among healhy elderly,MCI,cMCI and alzheimer's disease patients:From the alzheimer's disease neuroimaging initiative(ADNI) database[J].Journal of Neuroscience Methods,2018,302:14-23. |
[18] | Farouk Y,Rady S.Supervised classification techniques for identifying Alzheimers disease [C]∥Proc of International Conference on Advanced Intelligent Systems and Informa- tics,2018:189-197. |
[19] | LeCun Y,Bottou L,Bengio Y,et al.Gradient-based learning applied to document recognition[J].Proceedings of the IEEE,1998,86(11):2278-2324. |
[20] | Sarraf S,DeSouza D D,Anderson J, et al. DeepAD:Alzheimer’s disease classification via deep convolutional neural networks using MRI and fMRI[J].BioRxiv preprint doi:https://doi.org/10.1101/070441,2016. |
[21] | Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks[J]. Communications of the ACM, 2017,60 (6):84-90. |
[22] | Afzal S,Maqsood M,Nazir F,et al.A data augmentation-based framework to handle class imbalance problem for Alzheimers stage detection[J].IEEE Access,2019,7:115528-115539. |
[23] | Jain R,Jain N,Aggarwal A,et al.Convolutional neural network based Alzheimers disease classification from magnetic resonance brain images[J].Cognitive Systems Research,2019,57:147-159. |
[24] | Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions[C]∥Proc of the 2015 Conference on Computer Vision and Pattern Recognition, 2015:1-9. |
[25] | He K M,Zhang X Y,Ren S Q,et al.Deep residual learning for image recognition[C]∥Proc of 2016 IEEE Conference on Computer Vision and Pattern Recognition,2016:770-778. |
[26] | Ji H H, Liu Z B, Yan W Q, et al.Early diagnosis of Alzheimers disease using deep learning[C]∥Proc of the 2nd International Conference on Control and Computer Vision,2019:87-91. |
[27] | Liu M H,Cheng D N,Yan W W.Classification of Alzheimer’s disease by combination of convolutional and recurrent neural networks using FDG-PET images[J].Frontiers in Neuroinformatics,2018,12:35. |
[28] | Huang G,Liu Z,van der Maaten L,et al.Densely connected convolutional networks[C]∥Proc of 2017 IEEE Conference on Computer Vision and Pattern Recognition,2017:2261-2269. |
[29] | Ji S W,Xu W,Yang M,et al.3D convolutional neural networks for human action recognition[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2013,35(1):221-231. |
[30] | Kompanek M,Tamajka M,Benesova W.Volumetric data augmentation as an effective tool in MRI classification using 3D convolutional neural network [C]∥Proc of 2019 International Conference on Systems,Signals and Image Processing(IWSSIP),2019:115-119. |
[31] | Barbaroux H,Feng X,Yang J,et al.Encoding human cortex using spherical CNNs—A study on Alzheimers disease classification[C]∥Proc of IEEE International Symposium on Biomedical Imaging,2020:1322-1325. |
[32] | Kim H W,Lee H E,Oh K,et al.Multi-slice representational learning of convolutional neural network for Alzheimers disease classification using positron emission tomography[J].Biomedical Engineering Online,2020,19(1):70. |
[33] | Tufail A B,Ma Y K,Zhang Q N.Binary classification of Alzheimers disease using sMRI imaging modality and deep learning[J].Journal of Digital Imaging,2020,33(5):1073-1090. |
[34] | Suganthe R C,Latha R S,Geetha M,et al.Diagnosis of Alzheimers disease from brain magnetic resonance imaging images using deep learning algorithms[J].Advances in Electrical and Computer Engineering,2020,20(3):57-64. |
[35] | Huang Y C, Xu J H,Zhou Y C,et al.Diagnosis of Alzheimers disease via multi-modality 3D convolutional neural network.[J].Frontiers in Neuroscience,2019,13:509. |
[36] | Forouzannezhad P,Abbaspour A,Li C F,et al.A deep neural network approach for early diagnosis of mild cognitive impairment using multiple features [C]∥Proc of 2018 17th IEEE International Conference on Machine Learning and Applications(ICMLA),2018:1341-1346. |
[37] | Kang L,Jiang J W,Huang J J,et al.Identifying early mild cognitive impairment by multi-modality MRI-based deep learning[J].Frontiers in Aging Neuroscience,2020,12:206. |
[38] | Khvostikov A,Aderghal K,Benois-Pineau J,et al.3D CNN-based classification using sMRI and MD-DTI images for Alzheimer disease studies[J].arXiv:1801.05968,2018. |
[39] | Hinrichs C,Singh V,Xu G F,et al.MKL for robust multi-modality AD classification[C]∥Proc of International Confernece on Medical Image Computing and Computer Assist- ed Intervention,2009:786-794. |
[40] | Chaddad A, Desrosiers C,Toews M.Local discriminative characterization of MRI for Alzheimers disease[C]∥Proc of IEEE International Symposium on Biomedical Imaging,2016:1-5. |
[41] | Malone I B,Cash D,Ridgway G R,et al.MIRIAD—Public release of a multiple time point Alzheimers MR imaging dataset[J].NeuroImage,2013,70:33-36. |
[42] | Lü Hong-meng,Zhao Di,Chi Xue-bin.Deep learning for early diagnosis of Alzheimer’s disease based on intensive AlexNet [J].Computer Science,2017,44(S1):50-60.(in Chinese) |
[43] | Plis S M,Hjelm D R,Salakhutdinov R,et al.Deep learning for neuroimaging:A validation study[J].arXiv:1312.5847,2013. |
[44] | Khan N M,Abraham N,Hon M.Transfer learning with intelligent training data selection for prediction of Alzheimers disease[J].IEEE Access,2019,7:72726-72735. |
[45] | Alam S,Kwon G,Kim J,et al.Twin SVM-based classification of Alzheimers disease using complex dual-tree wavelet principal coefficients and LDA[J].Journal of Healthcare Engineering,2017,2017,Article ID:8750506. |
[46] | Cui R X,Liu M H.Hippocampus analysis by combination of 3D DenseNet and shapes for Alzheimers disease diagnosis[J].IEEE Journal of Biomedical and Health Informatics,2019,23(5):2099-2107. |
[47] | Aderghal K,Benois-Pineau J,Afdel K,et al.FuseMe:Classification of sMRI images by fusion of deep CNNs in 2D+ epsilon projections [M].New York:Assoc Computing Machinery,2017. |
[48] | PereznGonzalez J,Azamar C,PinanRamirez O.Automatic classification of Alzheimers disease based on CPD brain point cloud registration for feature extraction[C]∥Proc of Annual International Conference of the IEEE Engineering in Medicine and Biology Society,2019:812-815. |
[49] | Hong X,Lin R J,Yang C H,et al.Predicting Alzheimers disease using LSTM[J].IEEE Access,2019,7:80893-80901. |
[50] | Ge C J,Qu Q X,Gu I Y,et al.Multi-stream multi-scale deep convolutional networks for Alzheimers disease detection using MR images[J].Neurocomputing,2019,350:60-69. |
[51] | Hojjati S H,Ebrahimzadeh A,Khazaee A,et al.Predicting conversion from MCI to AD by integrating rs-fMRI and structural MRI[J].Computers in Biology and Medicine,2018,102:30-39. |
[52] | Wang H F,Shen Y Y,Wang S Q,et al.Ensemble of 3D densely connected convolutional network for diagnosis of mild cognitive impairment and Alzheimers disease[J].Neurocomputing,2019,333:145-156. |
[53] | Ortiz A,Munilla J,Gorriz J M,et al.Ensembles of deep learning architectures for the early diagnosis of the Alzheimers disease[J].International Journal of Neural Systems,2016,26(7):16500257. |
[54] | Li F, Tran L,Thung K,et al.A robust deep model for improved classification of AD/MCI patients[J].IEEE Journal of Biomedical and Health Informatics,2015,19(5):1610-1616. |
[55] | Yigit A,Isik Z.Applying deep learning models to structural MRI for stage prediction of Alzheimers disease[J].Turkish Journal of Electrical Engineering and Computer Sciences,2020,28(1):196-210. |
[56] | Lee N Q K,Huynh T T H,Yapp E K Y,et al.Identification of clathrin proteins by incorporating hyperparameter optimization in deep learning and PSSM profiles[J].Computer Methods and Programs in Biomedicine,2019,177:81-88. |
[57] | Ebrahimi-Ghahnavieh A,Luo S,Chiong R.Transfer learning for Alzheimers disease detection on MRI images[C]∥Proc of 2019 IEEE International Conference on Industry 4.0,Artifical Intelligence, and Communications Technology,2019:133-138. |
[58] | Zhang F, Li Z Z,Zhang B Y,et al.Multi-modal deep learning model for auxiliary diagnosis of Alzheimer’s disease[J].Neurocomputing,2019,361:185-195. |
[59] | Vu T D, Yang H, Nguyen V Q,et al.Multimodal learning using convolution neural network and sparse autoencoder[C]∥Proc of International Conference on Big Data and Smart Computing,2017:309-312. |
[60] | Zhang T, Shi M Y.Multi-modal neuroimaging feature fusion for diagnosis of Alzheimers disease[J].Journal of Neuroscience Methods,2020,341:108795. |
[61] | Feng C Y,Elazab A,Yang P,et al.Deep learning framework for Alzheimers disease diagnosis via 3D-CNN and FSBi-LSTM[J].IEEE Access,2019,7:63605-63618. |
[62] | Yan Yu,Lee H,Somer E,et al.Generation of Amyloid PET images via conditional adversarial training for predicting progression to Alzheimers disease [C]∥Proc of International Workshop on Predictive Intelligence in Medicine,2018:26-33. |
[63] | Murray M E,Graff-Radford N R,Ross O A,et al.Neuropathologically defined subtypes of Alzheimers disease with distinct clinical characteristics:A retrospective study[J].The Lancet Neurology,2011,10(9):785-796. |
附中文参考文献: | |
[42] | 吕鸿蒙,赵地,迟学斌.基于增强AlexNet的深度学习的阿尔茨海默病的早期诊断[J].计算机科学,2017,44(S1):50-60. |
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