Transfer learning on T1-weighted images for brain age estimation

Haitao Jiang; Jiajia Guo; Hongwei Du; Jinzhang Xu; Bensheng Qiu; Haitao Jiang; Jiajia Guo; Hongwei Du; Jinzhang Xu; Bensheng Qiu

doi:10.3934/mbe.2019218

Mathematical Biosciences and Engineering

2019, Volume 16, Issue 5: 4382-4398. doi: 10.3934/mbe.2019218

Previous Article Next Article

Research article Special Issues

Transfer learning on T1-weighted images for brain age estimation

1.
University of Science and Technology of China, Hefei, Anhui 230026, China
2.
School of Electrical Engineering and Automation, Hefei University of Technology, Hefei, Anhui 230009, China

Received: 10 January 2019 Accepted: 04 May 2019 Published: 17 May 2019

Due to both the hidden nature and the irreversibility of Alzheimers disease (AD), it has become the killer of the elderly and is thus the focus of much attention in the medical field. Radiologists compare the predicted brain age with the ground truth in order to provide a preliminary analysis of AD, which helps doctors diagnose AD as early in its development as possible. In this paper, a transfer learning-based method of predicting brain age using MR images and dataset of a public brain was proposed. In order to get the best transfer results, we froze different layers and only fine-tuned the remaining layers. We used three planes of brain MR images together to predict age for the first time and experiment results showed that the proposed method performs better than the state-of-the-art method under mean absolute error metric by 0.6 years. In addition, to explore the relationship between brain MR images of different planes and predicted age accuracy, we used three different planes of brain MR images to predict age respectively for the first time and found that sagittal plane MR images outperformed two other planes in age estimation. Finally, our research identified, the effective regions that contribute to brain age estimation for cognitively normal individuals and for AD patients with deep learning. For AD patients, the effective region is mainly concentrated in the frontal lobe of the brain, verifying the relevant medical conclusions about AD.
- Alzheimer’s disease,
- brain age,
- MR images,
- transfer learning,
- frontal lobe
Citation: Haitao Jiang, Jiajia Guo, Hongwei Du, Jinzhang Xu, Bensheng Qiu. Transfer learning on T1-weighted images for brain age estimation[J]. Mathematical Biosciences and Engineering, 2019, 16(5): 4382-4398. doi: 10.3934/mbe.2019218

Related Papers:

Abstract

Due to both the hidden nature and the irreversibility of Alzheimers disease (AD), it has become the killer of the elderly and is thus the focus of much attention in the medical field. Radiologists compare the predicted brain age with the ground truth in order to provide a preliminary analysis of AD, which helps doctors diagnose AD as early in its development as possible. In this paper, a transfer learning-based method of predicting brain age using MR images and dataset of a public brain was proposed. In order to get the best transfer results, we froze different layers and only fine-tuned the remaining layers. We used three planes of brain MR images together to predict age for the first time and experiment results showed that the proposed method performs better than the state-of-the-art method under mean absolute error metric by 0.6 years. In addition, to explore the relationship between brain MR images of different planes and predicted age accuracy, we used three different planes of brain MR images to predict age respectively for the first time and found that sagittal plane MR images outperformed two other planes in age estimation. Finally, our research identified, the effective regions that contribute to brain age estimation for cognitively normal individuals and for AD patients with deep learning. For AD patients, the effective region is mainly concentrated in the frontal lobe of the brain, verifying the relevant medical conclusions about AD.

References

[1]	L. Pini, M. Pievani, M. Bocchetta, et al., Brain atrophy in alzheimers disease and aging, Ageing Res. Rev., 30 (2016), 25–48.
[2]	A. Hosny, C. Parmar, J. Quackenbush, et al., Artificial intelligence in radiology, Nat. Rev. Cancer, (2018), 1.
[3]	A. Rios and R. Kavuluru, Ordinal convolutional neural networks for predicting rdoc positive valence psychiatric symptom severity scores, J. Biomed. Inform., 75 (2017), S85–S93.
[4]	O. Ronneberger, P. Fischer and T. Brox, U-net: Convolutional networks for biomedical image segmentation, in International Conference on Medical image computing and computer-assisted intervention, Springer, 2015, 234–241.
[5]	B. Harangi, Skin lesion classification with ensembles of deep convolutional neural networks, J. Biomed. Inform., 86 (2018), 25–32.
[6]	S. Zhang, E. Grave, E. Sklar, et al., Longitudinal analysis of discussion topics in an online breast cancer community using convolutional neural networks, J. Biomed. Inform., 69 (2017), 1–9.
[7]	J. Xi and A. Li, Discovering recurrent copy number aberrations in complex patterns via non- negative sparse singular value decomposition, IEEE/ACM TCBB, 13 (2016), 656–668.
[8]	J. Xi, M. Wang and A. Li, Discovering mutated driver genes through a robust and sparse co- regularized matrix factorization framework with prior information from mrna expression patterns and interaction network, BMC Bioinform., 19 (2018), 214.
[9]	J. Yosinski, J. Clune, Y. Bengio, et al., How transferable are features in deep neural networks?, in Adv. Neural Inf. Process Syst., 2014, 3320–3328.
[10]	K. He, R. Girshick and P. Dollr, Rethinking imagenet pre-training, arXiv preprint arXiv:1811.08883.
[11]	P. Lakhani and B. Sundaram, Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks, Radiology, 284 (2017), 574–582.
[12]	D. S. Kermany, M. Goldbaum, W. Cai, et al., Identifying medical diagnoses and treatable diseases by image-based deep learning, Cell, 172 (2018), 1122–1131.
[13]	I. Banerjee, A. Crawley, M. Bhethanabotla, et al., Transfer learning on fused multiparametric mr images for classifying histopathological subtypes of rhabdomyosarcoma, Comput. Med. Imag. Grap., 65 (2018), 167–175.
[14]	G. Huang, Z. Liu, L. Van Der Maaten, et al., Densely connected convolutional networks., in CVPR, vol. 1, 2017, 3.
[15]	Y. Taki, B. Thyreau, S. Kinomura, et al., Correlations among brain gray matter volumes, age, gender, and hemisphere in healthy individuals, PlOS ONE, 6 (2011), e22734.
[16]	C. Kondo, K. Ito, K. Wu, et al., An age estimation method using brain local features for t1- weighted images, in Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE, IEEE, 2015, 666–669.
[17]	K. Franke, G. Ziegler, S. Klöppel, et al., Estimating the age of healthy subjects from t1-weighted mri scans using kernel methods: exploring the influence of various parameters, Neuroimage, 50 (2010), 883–892.
[18]	B. Wang and T. D. Pham, Mri-based age prediction using hidden markov models, J. Neurosci. Meth., 199 (2011), 140–145.
[19]	J. Wang, W. Li, W. Miao, et al., Age estimation using cortical surface pattern combining thickness with curvatures, Med. Biol. Eng. Comput., 52 (2014), 331–341.
[20]	J. Xi, A. Li and M. Wang, A novel unsupervised learning model for detecting driver genes from pan-cancer data through matrix tri-factorization framework with pairwise similarities constraints, Neurocomputing, 296 (2018), 64–73.
[21]	J. H. Cole, R. P. Poudel, D. Tsagkrasoulis, et al., Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker, NeuroImage, 163 (2017), 115–124.
[22]	T.-W. Huang, H.-T. Chen, R. Fujimoto, et al., Age estimation from brain mri images using deep learning, in Biomedical Imaging (ISBI 2017), 2017 IEEE 14th International Symposium on, IEEE, 2017, 849–852.
[23]	H. Li, T. D. Satterthwaite and Y. Fan, Brain age prediction based on resting-state functional connectivity patterns using convolutional neural networks, in Biomedical Imaging (ISBI 2018), 2018 IEEE 15th International Symposium on, IEEE, 2018, 101–104.
[24]	A. Giorgio, L. Santelli, V. Tomassini, et al., Age-related changes in grey and white matter structure throughout adulthood, Neuroimage, 51 (2010), 943–951.
[25]	T.T.Brown, J.M.Kuperman, Y.Chung, etal., Neuroanatomical assessment of biological maturity, Current Biol., 22 (2012), 1693–1698.
[26]	G. Erus, H. Battapady, T. D. Satterthwait, et al., Imaging patterns of brain development and their relationship to cognition, Cerebral Cortex, 25 (2014), 1676–1684.
[27]	A. Krizhevsky, I. Sutskever and G. E. Hinton, Imagenet classification with deep convolutional neural networks, in Adv. Neural Inf. Process Syst., 2012, 1097–1105.
[28]	S. Ioffe and C. Szegedy, Batch normalization: Accelerating deep network training by reducing internal covariate shift, arXiv preprint arXiv:1502.03167.
[29]	J. Xing, K. Li, W. Hu, et al., Diagnosing deep learning models for high accuracy age estimation from a single image, Patt. Recogn., 66 (2017), 106–116.
[30]	J. Guo, H. Du, J. Zhu, et al., Relative location prediction in ct scan images using convolutional neural networks, Comput. Meth. Prog. Bio., 160 (2018), 43–49.
[31]	X. Glorot, A. Bordes and Y. Bengio, Deep sparse rectifier neural networks, in Proceedings of the fourteenth international conference on artificial intelligence and statistics, 2011, 315–323.
[32]	D. B. Larson, M. C. Chen, M. P. Lungren, et al., Performance of a deep-learning neural network model in assessing skeletal maturity on pediatric hand radiographs, Radiology, 287 (2017), 313– 322.
[33]	J. L. Whitwell, Progression of atrophy in alzheimers disease and related disorders, Neurotox Res., 18 (2010), 339–346.
[34]	F.Agosta, M.Pievani, S.Sala, etal., White matter damage in alzheimer disease and its relationship to gray matter atrophy, Radiology, 258 (2011), 853–863.
[35]	G. F. Busatto, G. E. Garrido, O. P. Almeida, et al., A voxel-based morphometry study of temporal lobe gray matter reductions in alzheimers disease, Neurobiol. Aging, 24 (2003), 221–231.
[36]	Z. Yao, Y. Zhang, L. Lin, et al., Abnormal cortical networks in mild cognitive impairment and alzheimer's disease, Plos Comput. Biol., 6 (2010), e1001006.

Reader Comments

Your name:*

Email:*
© 2019 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)