publication . Conference object . Preprint . 2019

3D-SiameseNet to Analyze Brain MRI

Ostertag, C.; Beurton-Aimar, M.; Urruty, T.;
Open Access English
  • Published: 08 Jul 2019
  • Publisher: HAL CCSD
  • Country: France
Abstract
Prediction of the cognitive evolution of a person susceptible to develop a neurodegenerative disorder is crucial to provide an appropriate treatment as soon as possible. In this paper we propose a 3D siamese network designed to extract features from whole-brain 3D MRI images. We show that it is possible to extract meaningful features using convolution layers, reducing the need of classical image processing operations such as segmentation or pre-computing features such as cortical thickness. To lead this study we used the Alzheimer's Disease Neuroimaging Initiative (ADNI), a public data base of 3D MRI brain images. A set of 247 subjects has been extracted, all of...
Subjects
free text keywords: [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, 68, Segmentation, Convolution, Brain mri, Cognitive evolution, Computer science, Pattern recognition, Artificial intelligence, business.industry, business, Cognitive score, Deep learning, Image processing, Neuroimaging
Funded by
NIH| Alzheimers Disease Neuroimaging Initiative
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 1U01AG024904-01
  • Funding stream: NATIONAL INSTITUTE ON AGING
,
CIHR
Project
  • Funder: Canadian Institutes of Health Research (CIHR)
Communities
Neuroinformatics
21 references, page 1 of 2

[1] G. B. Frisoni, N. C. Fox, C. R. Jack Jr, P. Scheltens, and P. M. Thompson, “The clinical use of structural mri in alzheimer disease,” Nature Reviews Neurology, vol. 6, no. 2, p. 67, 2010.

[2] N. Bhagwat, J. D. Viviano, A. N. Voineskos, M. M. Chakravarty, et al., “Modeling and prediction of clinical symptom trajectories in alzheimers disease using longitudinal data,” PLoS computational biology, vol. 14, no. 9, p. e1006376, 2018.

[3] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE, vol. 86, no. 11, pp. 2278- 2324, 1998.

[4] G. Koch, R. Zemel, and R. Salakhutdinov, “Siamese neural networks for one-shot image recognition,” in ICML Deep Learning Workshop, vol. 2, 2015.

[5] S. Zagoruyko and N. Komodakis, “Learning to compare image patches via convolutional neural networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4353-4361, 2015. [OpenAIRE]

[6] S. Lin, Z. Zhao, and F. Su, “Homemade ts-net for automatic face recognition,” in Proceedings of the 2016 ACM on International Conference on Multimedia Retrieval, pp. 135-142, ACM, 2016.

[7] S. Sarraf and G. Tofighi, “Classification of alzheimer's disease using fmri data and deep learning convolutional neural networks,” arXiv preprint arXiv:1603.08631, 2016. [OpenAIRE]

[8] C. D. Billones, O. J. L. D. Demetria, D. E. D. Hostallero, and P. C. Naval, “Demnet: A convolutional neural network for the detection of alzheimer's disease and mild cognitive impairment,” in Region 10 Conference (TENCON), 2016 IEEE, pp. 3724-3727, IEEE, 2016. [OpenAIRE]

[9] E. Hosseini-Asl, G. Gimel'farb, and A. El-Baz, “Alzheimer's disease diagnostics by a deeply supervised adaptable 3d convolutional network,” arXiv preprint arXiv:1607.00556, 2016.

[10] A. Payan and G. Montana, “Predicting alzheimer's disease: a neuroimaging study with 3d convolutional neural networks,” arXiv preprint arXiv:1502.02506, 2015. [OpenAIRE]

[11] S. Korolev, A. Safiullin, M. Belyaev, and Y. Dodonova, “Residual and plain convolutional neural networks for 3d brain mri classification,” in Biomedical Imaging (ISBI 2017), 2017 IEEE 14th International Symposium on, pp. 835-838, IEEE, 2017. [OpenAIRE]

[12] A. Khvostikov, K. Aderghal, J. Benois-Pineau, A. Krylov, and G. Catheline, “3d cnn-based classification using smri and md-dti images for alzheimer disease studies,” arXiv preprint arXiv:1801.05968, 2018.

[13] K. Aderghal, J. Benois-Pineau, and K. Afdel, “Classification of smri for alzheimer's disease diagnosis with cnn: Single siamese networks with 2d+? approach and fusion on adni,” in Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval, pp. 494-498, ACM, 2017. [OpenAIRE]

[14] S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” arXiv preprint arXiv:1502.03167, 2015.

[15] A. L. Maas, A. Y. Hannun, and A. Y. Ng, “Rectifier nonlinearities improve neural network acoustic models,” in Proc. icml, vol. 30, p. 3, 2013.

21 references, page 1 of 2
Abstract
Prediction of the cognitive evolution of a person susceptible to develop a neurodegenerative disorder is crucial to provide an appropriate treatment as soon as possible. In this paper we propose a 3D siamese network designed to extract features from whole-brain 3D MRI images. We show that it is possible to extract meaningful features using convolution layers, reducing the need of classical image processing operations such as segmentation or pre-computing features such as cortical thickness. To lead this study we used the Alzheimer's Disease Neuroimaging Initiative (ADNI), a public data base of 3D MRI brain images. A set of 247 subjects has been extracted, all of...
Subjects
free text keywords: [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, 68, Segmentation, Convolution, Brain mri, Cognitive evolution, Computer science, Pattern recognition, Artificial intelligence, business.industry, business, Cognitive score, Deep learning, Image processing, Neuroimaging
Funded by
NIH| Alzheimers Disease Neuroimaging Initiative
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 1U01AG024904-01
  • Funding stream: NATIONAL INSTITUTE ON AGING
,
CIHR
Project
  • Funder: Canadian Institutes of Health Research (CIHR)
Communities
Neuroinformatics
21 references, page 1 of 2

[1] G. B. Frisoni, N. C. Fox, C. R. Jack Jr, P. Scheltens, and P. M. Thompson, “The clinical use of structural mri in alzheimer disease,” Nature Reviews Neurology, vol. 6, no. 2, p. 67, 2010.

[2] N. Bhagwat, J. D. Viviano, A. N. Voineskos, M. M. Chakravarty, et al., “Modeling and prediction of clinical symptom trajectories in alzheimers disease using longitudinal data,” PLoS computational biology, vol. 14, no. 9, p. e1006376, 2018.

[3] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE, vol. 86, no. 11, pp. 2278- 2324, 1998.

[4] G. Koch, R. Zemel, and R. Salakhutdinov, “Siamese neural networks for one-shot image recognition,” in ICML Deep Learning Workshop, vol. 2, 2015.

[5] S. Zagoruyko and N. Komodakis, “Learning to compare image patches via convolutional neural networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4353-4361, 2015. [OpenAIRE]

[6] S. Lin, Z. Zhao, and F. Su, “Homemade ts-net for automatic face recognition,” in Proceedings of the 2016 ACM on International Conference on Multimedia Retrieval, pp. 135-142, ACM, 2016.

[7] S. Sarraf and G. Tofighi, “Classification of alzheimer's disease using fmri data and deep learning convolutional neural networks,” arXiv preprint arXiv:1603.08631, 2016. [OpenAIRE]

[8] C. D. Billones, O. J. L. D. Demetria, D. E. D. Hostallero, and P. C. Naval, “Demnet: A convolutional neural network for the detection of alzheimer's disease and mild cognitive impairment,” in Region 10 Conference (TENCON), 2016 IEEE, pp. 3724-3727, IEEE, 2016. [OpenAIRE]

[9] E. Hosseini-Asl, G. Gimel'farb, and A. El-Baz, “Alzheimer's disease diagnostics by a deeply supervised adaptable 3d convolutional network,” arXiv preprint arXiv:1607.00556, 2016.

[10] A. Payan and G. Montana, “Predicting alzheimer's disease: a neuroimaging study with 3d convolutional neural networks,” arXiv preprint arXiv:1502.02506, 2015. [OpenAIRE]

[11] S. Korolev, A. Safiullin, M. Belyaev, and Y. Dodonova, “Residual and plain convolutional neural networks for 3d brain mri classification,” in Biomedical Imaging (ISBI 2017), 2017 IEEE 14th International Symposium on, pp. 835-838, IEEE, 2017. [OpenAIRE]

[12] A. Khvostikov, K. Aderghal, J. Benois-Pineau, A. Krylov, and G. Catheline, “3d cnn-based classification using smri and md-dti images for alzheimer disease studies,” arXiv preprint arXiv:1801.05968, 2018.

[13] K. Aderghal, J. Benois-Pineau, and K. Afdel, “Classification of smri for alzheimer's disease diagnosis with cnn: Single siamese networks with 2d+? approach and fusion on adni,” in Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval, pp. 494-498, ACM, 2017. [OpenAIRE]

[14] S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” arXiv preprint arXiv:1502.03167, 2015.

[15] A. L. Maas, A. Y. Hannun, and A. Y. Ng, “Rectifier nonlinearities improve neural network acoustic models,” in Proc. icml, vol. 30, p. 3, 2013.

21 references, page 1 of 2
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