Detection algorithm of amyloid β-protein deposition in magnetic resonance image based on pixel feature learning method_Journal of Biomedical Engineering

Authors：

YAN Fang ¹ ,  LI Yongming ^1,2,3 , ZHU Xueru ¹ , WANG Jie ¹ , WANG Pin ¹ , LI Fan ¹ , QIU Mingguo ³ , QIN Jian ¹

1. College of Communication Engineering, Chongqing University, Chongqing 400044, P.R.China;
2. Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing 400044, P.R.China;
3. Department of Medical Image, College of Biomedical Engineering, Third Military Medical University, Chongqing 400038, P.R.China;

Corresponding author：

LI Yongming, Email: yongmingli@cqu.edu.cn

Keywords：

Alzheimer’s disease; amyloid β-protein deposition; magnetic resonance imaging; pixel feature learning; detection

DOI：

10.7507/1001-5515.201603061

Video：

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Amyloid β-protein (Aβ) deposition is an important prevention and treatment target for Alzheimer’s disease (AD), and early detection of Aβ deposition in the brain is the key to early diagnosis of AD. Magnetic resonance imaging (MRI) is the perfect imaging technology for the clinical diagnosis of AD, but it cannot display the plaque deposition directly. In this paper, based on two feature selection modes-filter and wrapper, chain-like agent genetic algorithm (CAGA), principal component analysis (PCA), support vector machine (SVM) and random forest (RF), we designed six kinds of feature learning classification algorithms to detect the information (distribution) of Aβ deposition through magnetic resonance image pixels selection. Firstly, we segmented the brain region from brain MR images. Secondly, we extracted the pixels in the segmented brain region as a feature vector (features) according to rows. Thirdly, we conducted feature learning on the extracted features, and obtained the final optimal feature subset by voting mechanism. Finally, using the final optimal selected features, we could find and mark the corresponding pixels on the MR images to show the information about Aβ plaque deposition by elastic mapping. According to the experimental results, the proposed pixel features learning methods in this paper could extract and reflect Aβ plaque deposition, and the best classification accuracy could be as high as 80%, thereby showing the effectiveness of the methods. The proposed methods can precisely detect the information of the Aβ plaque deposition, thereby being helpful for improving classification accuracy of diagnosis of AD.

Citation： YAN Fang, LI Yongming, ZHU Xueru, WANG Jie, WANG Pin, LI Fan, QIU Mingguo, QIN Jian. Detection algorithm of amyloid β-protein deposition in magnetic resonance image based on pixel feature learning method. Journal of Biomedical Engineering, 2017, 34(3): 431-438. doi: 10.7507/1001-5515.201603061 Copy

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8.	Small G W, Kepe V, Ercoli L M, et al. PET of brain amyloid and tau in mild cognitive impairment. New Engl J Med, 2006, 355(25): 2652-2663.
9.	董丽华, 陈翼, 罗霞. PET 显像仪临床应用的广泛性与局限性分析. 西南军医, 2010, 12(3): 475-476.
10.	Brix G, Lechel U, Glatting G, et al. Radiation exposure of patients undergoing whole-body dual-modality 18F-FDG PET/CT examinations. J Nucl Med, 2005, 46(4): 608-613.
11.	王军, 张莹, 郑妍鹏, 等. 阿尔茨海默病早期诊断: 影像学和体液生物标志物研究前沿和新动态. 中国医学前沿杂志: 电子版, 2012, 4(10): 36-47.
12.	林岚, 郝冬梅, 白燕萍, 等. 7 T MRI 系统在脑图像中的应用研究进展. 生物医学工程学杂志, 2013, 30(5): 1127-1130.
13.	王啸. 阿尔茨海默病 Aβ 斑块 MR 成像研究. 国际医学放射学杂志, 2012, 35(6): 519-522.
14.	Wadghiri Y Z, Hoang D M, Wisniewski T, et al. In vivo magnetic resonance imaging of amyloid-β plaques in mice. Methods Mol Biol, 2012, 849: 435-451.
15.	Teipel S J, Kaza E, Hadlich S, et al. Automated detection of amyloid-β-related cortical and subcortical signal changes in a transgenic model of Alzheimer’s disease using high-field MRI. J Alzheimers Dis, 2011, 23(2): 221-237.
16.	Li G, Yang X F, Yang E S. P4-007: High field MRI study of the beta amyloid and plaque deposits in the brain of APP/PS1 mice. Alzheimers & Dementia, 2006, 2(3): S515-S516.
17.	Hardy J, Selkoe D J. The amyloid hypothesis of Alzheimer’s disease: progress and problems on the road to therapeutics. Science, 2002, 297(5580): 353-356.
18.	Meadowcroft M D, Connor J R, Smith M B, et al. MRI and histological analysis of beta-amyloid plaques in both human Alzheimer’s disease and APP/PS1 transgenic mice. J Magn Reson Imaging, 2009, 29(5): 997-1007.
19.	Zhang Jiangyang, Yarowsky P, Gordon M N, et al. Detection of amyloid plaques in mouse models of Alzheimer’s disease by magnetic resonance imaging. Magn Reson Med, 2004, 51(3): 452-457.
20.	Lee S P, Falangola M F, Nixon R A, et al. Visualization of β-amyloid plaques in a transgenic mouse model of Alzheimer’s disease using MR microscopy without contrast reagents. Magn Reson Med, 2004, 52(3): 538-544.
21.	Li Yongming, Zeng Xiaoping, Han Liang, et al. Two coding based adaptive parallel co-genetic algorithm with double agents structure. Eng Appl Artif Intell, 2010, 23(4): 526-542.
22.	Zhou Zhihua, Wu Jianxin, Tang Wei. Ensembling neural networks: Many could be better than all. Artif Intell, 2002, 137(1/2): 239-263.

1. Small D H, Mclean C A. Alzheimer’s disease and the amyloid β protein. J Neurochem, 2004, 6(2): 173-174.
2. 生物谷 BIOON. 提前 20 年检测阿兹海默症(2016-03-18)[2016-04-17]. http://news.bioon.com/article/6680198.html.
3. Antonios G, Borgers H, Richard B C, et al. Alzheimer therapy with an antibody against N-terminal Abeta 4-X and pyroglutamate Abeta 3-X. Sci Rep, 2015, 5.
4. Jankowsky J L, Slunt H H, Gonzales V, et al. Persistent amyloidosis following suppression of Aβ production in a transgenic model of Alzheimer disease. PLoS Med, 2005, 2(12): 1318-1333.
5. Kohannim O, Hua X, Hibar D P, et al. Boosting power for clinical trials using classifiers based on multiple biomarkers[J]. Neurobiology of Aging, 2010, 31(8): 1429-1442.
6. van Waalwijk van Doorn L J C, Koel-Simmelink M J, Haußmann U, et al. Validation of soluble amyloid-β precursor protein assays as diagnostic CSF biomarkers for neurodegenerative diseases. J Neurochem, 2016, 137(1): 112-121.
7. Johnson K A, Sperling R A, Gidicsin C M, et al. Florbetapir (F18-AV-45) PET to assess amyloid burden in Alzheimer’s disease dementia, mild cognitive impairment, and normal aging. Alzheimers Dement, 2013, 9(5 Suppl): S72-S83.
8. Small G W, Kepe V, Ercoli L M, et al. PET of brain amyloid and tau in mild cognitive impairment. New Engl J Med, 2006, 355(25): 2652-2663.
9. 董丽华, 陈翼, 罗霞. PET 显像仪临床应用的广泛性与局限性分析. 西南军医, 2010, 12(3): 475-476.
10. Brix G, Lechel U, Glatting G, et al. Radiation exposure of patients undergoing whole-body dual-modality 18F-FDG PET/CT examinations. J Nucl Med, 2005, 46(4): 608-613.
11. 王军, 张莹, 郑妍鹏, 等. 阿尔茨海默病早期诊断: 影像学和体液生物标志物研究前沿和新动态. 中国医学前沿杂志: 电子版, 2012, 4(10): 36-47.
12. 林岚, 郝冬梅, 白燕萍, 等. 7 T MRI 系统在脑图像中的应用研究进展. 生物医学工程学杂志, 2013, 30(5): 1127-1130.
13. 王啸. 阿尔茨海默病 Aβ 斑块 MR 成像研究. 国际医学放射学杂志, 2012, 35(6): 519-522.
14. Wadghiri Y Z, Hoang D M, Wisniewski T, et al. In vivo magnetic resonance imaging of amyloid-β plaques in mice. Methods Mol Biol, 2012, 849: 435-451.
15. Teipel S J, Kaza E, Hadlich S, et al. Automated detection of amyloid-β-related cortical and subcortical signal changes in a transgenic model of Alzheimer’s disease using high-field MRI. J Alzheimers Dis, 2011, 23(2): 221-237.
16. Li G, Yang X F, Yang E S. P4-007: High field MRI study of the beta amyloid and plaque deposits in the brain of APP/PS1 mice. Alzheimers & Dementia, 2006, 2(3): S515-S516.
17. Hardy J, Selkoe D J. The amyloid hypothesis of Alzheimer’s disease: progress and problems on the road to therapeutics. Science, 2002, 297(5580): 353-356.
18. Meadowcroft M D, Connor J R, Smith M B, et al. MRI and histological analysis of beta-amyloid plaques in both human Alzheimer’s disease and APP/PS1 transgenic mice. J Magn Reson Imaging, 2009, 29(5): 997-1007.
19. Zhang Jiangyang, Yarowsky P, Gordon M N, et al. Detection of amyloid plaques in mouse models of Alzheimer’s disease by magnetic resonance imaging. Magn Reson Med, 2004, 51(3): 452-457.
20. Lee S P, Falangola M F, Nixon R A, et al. Visualization of β-amyloid plaques in a transgenic mouse model of Alzheimer’s disease using MR microscopy without contrast reagents. Magn Reson Med, 2004, 52(3): 538-544.
21. Li Yongming, Zeng Xiaoping, Han Liang, et al. Two coding based adaptive parallel co-genetic algorithm with double agents structure. Eng Appl Artif Intell, 2010, 23(4): 526-542.
22. Zhou Zhihua, Wu Jianxin, Tang Wei. Ensembling neural networks: Many could be better than all. Artif Intell, 2002, 137(1/2): 239-263.

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