Patch-Based DTI Grading: Application to Alzheimer’s Disease Classification

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• 关键词：Patch ; based grading ; Alzheimer’s disease classification ; DTI ; DWI ; Mild cognitive impairment
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9993
• 期：1
• 页码：76-83
• 全文大小：918 KB
• 参考文献：1.Frisoni, G.B., et al.: The clinical use of structural MRI in Alzheimer disease. Nat. Rev. Neurol. 6(2), 67–77 (2010)CrossRef
  2.Cuingnet, R., et al.: Automatic classification of patients with Alzheimer’s disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage 56(2), 766–781 (2011)CrossRef
  3.Wolz, R., et al.: Multi-method analysis of MRI images in early diagnostics of Alzheimer’s disease. PloS ONE 6(10), e25446 (2011)CrossRef
  4.Coupé, P., et al.: Scoring by nonlocal image patch estimator for early detection of Alzheimer’s disease. NeuroImage: Clin. 1(1), 141–152 (2012)CrossRef
  5.Liu, M., et al.: Ensemble sparse classification of Alzheimer’s disease. NeuroImage 60(2), 1106–1116 (2012)CrossRef
  6.Tong, T., et al.: Multiple instance learning for classification of dementia in brain MRI. Med. Image Anal. 18(5), 808–818 (2014)CrossRef
  7.Komlagan, M., et al.: Anatomically constrained weak classifier fusion for early detection of Alzheimer’s disease. In: Wu, G., Zhang, D., Zhou, L. (eds.) MLMI 2014. LNCS, vol. 8679, pp. 141–148. Springer, Heidelberg (2014)
  8.Coupé, P., et al.: Detection of Alzheimer’s disease signature in MR images seven years before conversion to dementia: toward an early individual prognosis. HBM 36(12), 4758–4770 (2015)CrossRef
  9.Koikkalainen, J., et al.: Differential diagnosis of neurodegenerative diseases using structural MRI data. NeuroImage: Clin. 11, 435–449 (2016)CrossRef
  10.Rose, S.E., et al.: Gray and white matter changes in Alzheimer’s disease: a diffusion tensor imaging study. J. Magn. Resonan. Imaging 27(1), 20–26 (2008)CrossRef
  11.Nir, T.M., et al.: Effectiveness of regional DTI measures in distinguishing Alzheimer’s disease, MCI, and normal aging. NeuroImage: Clin. 3, 180–195 (2013)CrossRef
  12.Wang, Z., et al.: Interhemispheric functional and structural disconnection in Alzheimers disease: a combined resting-state f MRI and DTI study. PloS ONE 10(5), e0126310 (2015)CrossRef
  13.Jung, W.B., et al.: Automated classification to predict the progression of Alzheimer’s disease using whole
  ain volumetry and DTI. Psychiatr. Investig. 12(1), 92–102 (2015)CrossRef
  14.Fellgiebel, A., et al.: Diffusion tensor imaging of the hippocampus in MCI and early Alzheimer’s disease. J. Alzheimer’s Dis. 26(s3), 257–262 (2011)
  15.Liu, Y., et al.: Diffusion tensor imaging and tract-based spatial statistics in Alzheimer’s disease and mild cognitive impairment. Neurobiol. Aging 32(9), 1558–1571 (2011)CrossRef
  16.Prasad, G., et al.: Brain connectivity and novel network measures for Alzheimer’s disease classification. Neurobiol. Aging 36, S121–S131 (2015)CrossRef
  17.Jahanshad, N., et al.: Diffusion tensor imaging in seven minutes: determining trade-offs between spatial and directional resolution. In: ISBI, pp. 1161–1164. IEEE (2010)
  18.Manjón, J.V., et al.: volBrain: an online MRI brain volumetry system. In: Organization for HBM, vol. 15 (2015)
  19.Manjón, J.V., et al.: Adaptive non-local means denoising of MR images with spatially varying noise levels. J. Magn. Reson. Imaging 31(1), 192–203 (2010)CrossRef
  20.Avants, B.B., et al.: A reproducible evaluation of ANTs similarity metric performance in brain image registration. NeuroImage 54(3), 2033–2044 (2011)CrossRef
  21.Tustison, N.J., et al.: N4ITK: improved N3 bias correction. IEEE Trans. Med. Imaging 29(6), 1310–1320 (2010)CrossRef
  22.Manjón, J.V., et al.: NICE: non-local intracranial cavity extraction. Int. J. Biomed. Imaging 2014 (2014). Article ID 820205
  23.Coupé, P., et al.: Patch-based segmentation using expert priors: application to hippocampus and ventricle segmentation. NeuroImage 54(2), 940–954 (2011)CrossRef
  24.Boccardi, M., et al.: Training labels for hippocampal segmentation based on the EADC-ADNI harmonized hippocampal protocol. Alzheimer’s Dement. 11(2), 175–183 (2015)CrossRef
  25.Manjón, J.V., et al.: Diffusion weighted image denoising using overcomplete local PCA. PloS ONE 8(9), e73021 (2013)CrossRef
  26.Basser, P.J., et al.: MR diffusion tensor spectroscopy and imaging. Biophys. J. 66(1), 259 (1994)CrossRef
  27.Garyfallidis, E., et al.: Dipy, a library for the analysis of diffusion MRI data. Front. Neuroinform. 8, 8 (2014)CrossRef
  28.Dukart, J., et al.: Age correction in dementia-matching to a healthy brain. PloS ONE 6(7), e22193 (2011)CrossRef
  
• 作者单位：Kilian Hett (18) (19)
  Vinh-Thong Ta (18) (19) (20)
  Rémi Giraud (18) (19) (21) (22)
  Mary Mondino (18) (19)
  José V. Manjón (23)
  Pierrick Coupé (18) (19)
  Alzheimer’s Disease Neuroimaging Initiative
  
  18. University of Bordeaux, LaBRI, UMR 5800 PICTURA, 33400, Talence, France
  19. CNRS, LaBRI, UMR 5800, PICTURA, 33400, Talence, France
  20. Bordeaux INP, LaBRI, UMR 5800, PICTURA, 33600, Pessac, France
  21. University of Bordeaux, IMB, UMR 5251, 33400, Talence, France
  22. CNRS, IMB, UMR 5251, 33400, Talence, France
  23. Universitat Politècnia de València, ITACA, 46022, Valencia, Spain
  
• 丛书名：Patch-Based Techniques in Medical Imaging
• ISBN：978-3-319-47118-1
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9993

文摘

Early diagnosis is one of the most important challenges related to Alzheimer’s disease (AD). To address this issue, numerous studies proposed biomarkers based on anatomical MRI. Among them, patch-based grading demonstrated state-of-the-art results when applied to T1-weighted MRI. In this work, we propose to use a similar framework on different diffusion parameters extracted from DTI. We also propose to use a fast patch-based search strategy to provide novel biomarkers for the early detection of AD. We intensively compare our new grading-based DTI features with basic MRI/DTI biomarkers and evaluate our method within a cross validation classification framework. Finally, we demonstrate that the proposed biomarkers obtain competitive results for the identification of the different stages of AD.