| ECR 2019 / C-2108 |
 |
| Influence of acquisition parameters on morphometric values obtained from structural magnetic resonance images | |
| Congress: | ECR 2019 |
| Poster No.: | C-2108 |
| Type: | Scientific Exhibit |
| Keywords: | Artificial Intelligence, Anatomy, Neuroradiology brain, MR, Segmentation, Computer Applications-General, Computer Applications-Detection, diagnosis, Dementia |
| Authors: | I. Evangelista, C. L. Galimberti, G. Pascariello, J. C. Gomez, A. L. Rodríguez Musso, P. Donnelly-Kehoe; Rosario/AR |
| DOI: | 10.26044/ecr2019/C-2108 |
| DOI-Link: | http://dx.doi.org/10.26044/ecr2019/C-2108 |
References
[1] Fischl, B. (2012). FreeSurfer. Neuroimage, 62(2), 774-781.
[2] Pedregosa, F. et al (2011). Scikit-learn: Machine Learning in Python, Journal of Machine Learning Research, 12, 2825-2830.
[3] P. A. Donnelly-Kehoe, G. Pascariello, J. C. Gomez. The changing brain in healthy aging: A multi-MRI machine and multicenter surface-based morphometry study. In Proceedings of SPIE - The International Society for Optical Engineering. Volume 10160, 2017. Article number 101600B.
[4] Donnelly-Kehoe, P. A., Pascariello, G. O., Gómez, J. C., & Alzheimers Disease Neuroimaging Initiative. (2018). Looking for Alzheimer's Disease morphometric signatures using machine learning techniques. Journal of neuroscience methods, 302, 24-34.
[5] Maaten, L. V. D., y Hinton, G. (2008). Visualizing data using t-SNE. Journal of machine learning research, 9(Nov), 2579-2605.
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