Abstract
The transference of information between entities is one of the most popular application of deep learning. It has been used to generate a stylized version of an image by combining a source image to another that determines the style of the final result. In the field of neuroimaging, different modalities are frequently available, providing structural or functional information. Those modalities are usually analyzed separately, although it is possible to jointly use features extracted from structural and functional neuroimage to improve the classification performance in Computer Aided Diagnosis (CAD) tools. In this paper we propose a method based on the principles of neural style transfer to combine information from Magnetic resonance Imaging (MRI) and Positron Emission Tomography (PET), generating a new image that contains structural and functional information. The usefulness of this method has been assessed with images from the Alzheimer Disease Neuroimaging Initiative, demonstrating that using the new mixed mode image outperforms the classification accuracy obtained by individual MRI or PET images.
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References
Alsop, D.C., Casement, M., de Bazelaire, C., Fong, T., Press, D.Z.: Hippocampal hyperperfusion in Alzheimer’s disease. Neuroimage 42(4), 1267–1274 (2008). http://www.sciencedirect.com/science/article/B6WNP-4SSG4W3-2/2/e86def44fdf4c58eb9cb2f58f16fcdc1
Alzheimer’s Disease Neuroimaging Initiative (2021). http://adni.loni.ucla.edu/. Accessed 5 Nov 2021
Chyzhyk, D., Graña, M., Savio, A., Maiora, J.: Hybrid dendritic computing with kernel-LICA applied to Alzheimer’s disease detection in MRI. Neurocomputing 75(1), 72–77 (2012). https://doi.org/10.1016/j.neucom.2011.02.024
Chételat, G., et al.: Amyloid-PET and 18F-FDG-PET in the diagnostic investigation of Alzheimer’s disease and other dementias. Lancet Neurol. 19(11), 951–962 (2020)
Cuingnet, R., et al.: Alzheimer’s Disease neuroimaging initiative: 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 (2010)
Górriz, J.M., et al.: Artificial intelligence within the interplay between natural and artificial computation: advances in data science, trends and applications. Neurocomputing 410, 237–270 (2020)
Álvarez, I., et al.: Alzheimer’s diagnosis using eigenbrains and support vector machines. In: Cabestany, J., Sandoval, F., Prieto, A., Corchado, J.M. (eds.) IWANN 2009. LNCS, vol. 5517, pp. 973–980. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-02478-8_122
Martinez-Murcia, F.J., Górriz, J.M., Ramírez, J., Ortiz, A., Disease Neuroimaging Initiative, et al.: A spherical brain mapping of MR images for the detection of Alzheimer’s disease. Curr. Alzheimer Res. 13(5), 575–588 (2016)
Jing, Y., Yang, Y., Feng, Z., Ye, J., Yu, Y., Song, M.: Neural style transfer: a review. IEEE Trans. Visual. Comput. Graph. 26(11), 3365–3385 (2020)
Martinez-Murcia, F.J., Ortiz, A., Gorriz, J.M., Ramirez, J., Castillo-Barnes, D.: Studying the manifold structure of Alzheimer’s disease: a deep learning approach using convolutional autoencoders. IEEE J. Biomed. Health Inform. 24(1), 17–26 (2020)
Ortiz, A., Górriz, J.M., Ramírez, J., Martinez-Murcia, F.J., Alzheimer’s Disease Neuroimaging Initiative et al.: Automatic ROI selection in structural brain MRI using SOM 3D projection. PLoS ONE 9(4), e93851 (2014)
Ortiz, A., Górriz, J.M., Ramírez, J., Martínez-Murcia, F.J.: LVQ-SVM based CAD tool applied to structural MRI for the diagnosis of the Alzheimer’s disease. Pattern Recogn. Lett. 34(14), 1725–1733 (2013). https://doi.org/10.1016/j.patrec.2013.04.014
Ortiz, A., Munilla, J., Górriz, J.M., Ramírez, J.: Ensembles of deep learning architectures for the early diagnosis of the Alzheimer’s disease. Int. J. Neural Syst. 26(07), 1650025 (2016)
Ortiz, A., Munilla, J., Álvarez Illán, I., Górriz, J.M., Ramírez, J., Alzheimer’s Disease Neuroimaging Initiative: Exploratory graphical models of functional and structural connectivity patterns for Alzheimer’s disease diagnosis. Front. Comput. Neurosci. 9, 132 (2015)
Rolls, E.T., Huang, C.C., Lin, C.P., Feng, J., Joliot, M.: Automated anatomical labelling atlas 3. Neuroimage 206, 116189 (2020)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Structural Brain Mapping Group. Department of Psychiatry (2014). http://dbm.neuro.uni-jena.de/vbm8/VBM8-Manual.pdf. Accessed 10 Mar 2014
Acknowledgments
This work was supported by projects PGC2018-098813-B-C32 and RTI2018-098913-B100 (Spanish “Ministerio de Ciencia, Innovación y Universidades”), UMA20-FEDERJA-086, CV20-45250, A-TIC-080-UGR18 and P20 00525 (Consejería de econnomía y conocimiento, Junta de Andalucía) and by European Regional Development Funds (ERDF). Work of J.E. Arco was supported by Ministerio de Universidades, Gobierno de España through grant “Margarita Salas”.
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Ortiz, A. et al. (2022). Towards Mixed Mode Biomarkers: Combining Structural and Functional Information by Deep Learning. In: Ferrández Vicente, J.M., Álvarez-Sánchez, J.R., de la Paz López, F., Adeli, H. (eds) Artificial Intelligence in Neuroscience: Affective Analysis and Health Applications. IWINAC 2022. Lecture Notes in Computer Science, vol 13258. Springer, Cham. https://doi.org/10.1007/978-3-031-06242-1_10
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