Abstract
Predicting future progression of brain disorders is fundamental for effective intervention of pathological cognitive decline. Structural MRI provides a non-invasive solution to examine brain pathology and has been widely used for longitudinal analysis of brain disorders. Previous studies typically use only complete baseline MRI scans to predict future disease status due to the lack of MRI data at one or more future time points. Since temporal changes of each brain MRI are ignored, these methods would result in sub-optimal performance. To this end, we propose a longitudinal-diagnostic generative adversarial network (LDGAN) to predict multiple clinical scores at future time points using incomplete longitudinal MRI data. Specifically, LDGAN imputes MR images by learning a bi-directional mapping between MRIs of two adjacent time points and performing clinical score prediction jointly, thereby explicitly encouraging task-oriented image synthesis. The proposed LDGAN is further armed with a temporal constraint and an output constraint to model the temporal regularity of MRIs at adjacent time points and encourage the diagnostic consistency, respectively. We also design a weighted loss function to make use of those subjects without ground-truth scores at certain time points. The major advantage of the proposed LDGAN is that it can impute those missing scans in a task-oriented manner and can explicitly capture the temporal characteristics of brain changes for accurate prediction. Experimental results on both ADNI-1 and ADNI-2 datasets demonstrate that, compared with the state-of-the-art methods, LDGAN can generate more reasonable MRI scans and efficiently predict longitudinal clinical measures.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Frisoni, G.B., Fox, N.C., Jack, C.R., Scheltens, P., Thompson, P.M.: The clinical use of structural MRI in Alzheimer disease. Nat. Rev. Neurol. 6(2), 67–77 (2010)
Yuan, L., Wang, Y., Thompson, P.M., Narayan, V.A., Ye, J.: Multi-source feature learning for joint analysis of incomplete multiple heterogeneous neuroimaging data. NeuroImage 61(3), 622–632 (2012)
Liu, M., Zhang, D., Shen, D.: Relationship induced multi-template learning for diagnosis of Alzheimer’s disease and mild cognitive impairment. IEEE Trans. Med. Imaging 35(6), 1463–1474 (2016)
Cheng, B., Liu, M., Shen, D., Li, Z., Zhang, D.: Multi-domain transfer learning for early diagnosis of Alzheimer’s disease. Neuroinformatics 15(2), 115–132 (2017)
Liu, M., Zhang, J., Adeli, E., Shen, D.: Landmark-based deep multi-instance learning for brain disease diagnosis. Med. Image Anal. 43, 157–168 (2018)
Lei, B., Jiang, F., Chen, S., Ni, D., Wang, T.: Longitudinal analysis for disease progression via simultaneous multi-relational temporal-fused learning. Front. Aging Neurosci. 9(6), 1–6 (2017)
Liu, M., Zhang, J., Lian, C., Shen, D.: Weakly supervised deep learning for brain disease prognosis using MRI and incomplete clinical scores. IEEE Trans. Cybern. 50(7), 3381–3392 (2020)
Weiner, M.W., et al.: The Alzheimer’s disease neuroimaging initiative: a review of papers published since its inception. Alzheimer’s Dement. 9(5), e111–e194 (2013)
Jiang, P., Wang, X., Li, Q., Jin, L., Li, S.: Correlation-aware sparse and low-rank constrained multi-task learning for longitudinal analysis of Alzheimer’s disease. IEEE J. Biomed. Health Inform. 23(4), 1450–1456 (2018)
Ritter, K., Schumacher, J., Weygandt, M., Buchert, R., Allefeld, C., Haynes, J.D.: Multimodal prediction of conversion to Alzheimer’s disease based on incomplete biomarkers. Alzheimer’s Dement.: Diagn. Assess. Dis. Monit. 1(2), 206–215 (2015)
Pan, Y., Liu, M., Lian, C., Zhou, T., Xia, Y., Shen, D.: Synthesizing missing PET from MRI with cycle-consistent generative adversarial networks for Alzheimer’s disease diagnosis. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11072, pp. 455–463. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00931-1_52
Beason-Held, L.L., O Goh, J., An, Y., A Kraut, M., J O’Brien, R., Ferrucci, L.: A longitudinal study of brain anatomy changes preceding dementia in down syndromechanges in brain function occur years before the onset of cognitive impairment. J. Neurosci. 33(46), 18008–18014 (2013)
Jie, B., Liu, M., Liu, J., Zhang, D., Shen, D.: Temporally constrained group sparse learning for longitudinal data analysis in Alzheimer’s disease. IEEE Trans. Biomed. Eng. 64(1), 238–249 (2016)
Pujol, J., et al.: A longitudinal study of brain anatomy changes preceding dementia in down syndrome. NeuroImage Clin. 18, 160–166 (2018)
Jack, C., Bernstein, M., Fox, N., et al.: The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods. J. Magn. Reson. Imaging 27(4), 685–691 (2008)
Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)
Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2223–2232 (2017)
Hore, A., Ziou, D.: Image quality metrics: PSNR vs. SSIM. In: International Conference on Pattern Recognition, pp. 2366–2369. IEEE (2010)
Zhang, J., Liu, M., An, L., Gao, Y., Shen, D.: Alzheimer’s disease diagnosis using landmark-based features from longitudinal structural MR images. IEEE J. Biomed. Health Inform. 21(6), 1607–1616 (2017)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Ning, Z., Zhang, Y., Pan, Y., Zhong, T., Liu, M., Shen, D. (2020). LDGAN: Longitudinal-Diagnostic Generative Adversarial Network for Disease Progression Prediction with Missing Structural MRI. In: Liu, M., Yan, P., Lian, C., Cao, X. (eds) Machine Learning in Medical Imaging. MLMI 2020. Lecture Notes in Computer Science(), vol 12436. Springer, Cham. https://doi.org/10.1007/978-3-030-59861-7_18
Download citation
DOI: https://doi.org/10.1007/978-3-030-59861-7_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-59860-0
Online ISBN: 978-3-030-59861-7
eBook Packages: Computer ScienceComputer Science (R0)
