Abstract
Longitudinal medical image data are becoming increasingly important for monitoring patient progression. However, such datasets are often small, incomplete, or have inconsistencies between observations. Thus, we propose a generative model that not only produces continuous trajectories of fully synthetic patient images, but also imputes missing data in existing trajectories, by estimating realistic progression over time. Our generative model is trained directly on features extracted from images and maps these into a linear trajectory in a Euclidean space defined with velocity, delay, and spatial parameters that are learned directly from the data. We evaluated our method on toy data and face images, both showing simulated trajectories mimicking progression in longitudinal data. Furthermore, we applied the proposed model on a complex neuroimaging database extracted from ADNI. All datasets show that the model is able to learn overall (disease) progression over time.
C. Chadebec and E. M. C. Huijben—Equal contribution.
S. Allassonnière and M. A. J. M. van Eijnatten—Equal contribution.
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Notes
- 1.
Code and dataset details are available at https://github.com/evihuijben/longVAE.
- 2.
Downloaded from https://doi.org/10.5281/zenodo.5081988.
- 3.
Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://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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Chadebec, C., Huijben, E.M.C., Pluim, J.P.W., Allassonnière, S., van Eijnatten, M.A.J.M. (2022). An Image Feature Mapping Model for Continuous Longitudinal Data Completion and Generation of Synthetic Patient Trajectories. In: Mukhopadhyay, A., Oksuz, I., Engelhardt, S., Zhu, D., Yuan, Y. (eds) Deep Generative Models. DGM4MICCAI 2022. Lecture Notes in Computer Science, vol 13609. Springer, Cham. https://doi.org/10.1007/978-3-031-18576-2_6
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