Abstract
When diagnosing patients suffering from dementia based on imaging data like PET scans, the identification of suitable predictive regions of interest (ROIs) is of great importance. We present a case study of 3-D Convolutional Neural Networks (CNNs) for the detection of ROIs in this context, just using voxel data, without any knowledge given a priori. Our results on data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) suggest that the predictive performance of the method is on par with that of state-of-the-art methods, with the additional benefit of potential insights into affected brain regions.
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- 1.
For a good overview see the recent paper by Vieira et al. [2]. Apparently many more papers can be found in online archives than papers that have appeared already.
- 2.
URL: http://adni.loni.usc.edu/ (visited on Jan. 12, 2016).
- 3.
Different \(\gamma \)s are possible. Here, we used \(\gamma _1 = \bar{v}_{xyz}\) (the average voxel of all scans in the data set) as well as \(\gamma _2 = round\left( \frac{{v}_{xyz}}{max(X)}\right) \).
References
Li, R., Perneczky, R., Drzezga, A., Kramer, S.: Gaussian mixture models and model selection for [18F] fluorodeoxyglucose positron emission tomography classification in Alzheimer’s disease. PLoS ONE 10(4), e0122731 (2015)
Vieira, S., Pinaya, W.H., Mechelli, A.: Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: Methods and applications. Neurosci. Biobehav. Rev. 74, (Part A), 58–75 (2017)
Jagust, W.J., Landau, S.M., Koeppe, R.A., Reiman, E.M., Chen, K., Mathis, C.A., Price, J.C., Foster, N.L., Wang, A.Y.: The ADNI PET core: 2015. Alzheimers Dement. 11(7), 757–771 (2015)
Acknowledgments
This work was partially supported by the Carl-Zeiss-Foundation Competence Center for High Performance Computing.
Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.
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Karwath, A., Hubrich, M., Kramer, S., the Alzheimer’s Disease Neuroimaging Initiative. (2017). Convolutional Neural Networks for the Identification of Regions of Interest in PET Scans: A Study of Representation Learning for Diagnosing Alzheimer’s Disease. In: ten Teije, A., Popow, C., Holmes, J., Sacchi, L. (eds) Artificial Intelligence in Medicine. AIME 2017. Lecture Notes in Computer Science(), vol 10259. Springer, Cham. https://doi.org/10.1007/978-3-319-59758-4_36
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DOI: https://doi.org/10.1007/978-3-319-59758-4_36
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