Abstract
Brain imaging plays a crucial role in the study and diagnosis of Alzheimer’s disease. However, obtaining brain imaging data is challenging due to the uneven quality of images and the need to consider patient privacy. Consequently, the available data sets are often small, which can limit the effectiveness of analyses and the generalizability of findings. This study proposes a conditional diffusion model-based method for generating brain images of Alzheimer’s disease and mild cognitive impairment. The generated data was evaluated for its classification performance by comparing it with datasets containing different proportions of generated data and other data augmentation methods. The performance was visualized to aid the analysis of the experimental results. The analysis of the experimental results showed that the generated data can be used as a reliable data supplement, as it was shown to be beneficial for the classification of Alzheimer’s disease. The proposed method offers a promising approach to generating synthetic data for brain imaging research, particularly in neurodegenerative disease diagnosis and treatment.
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
References
Weidner, W.S., Barbarino, P.: P4-443: the state of the art of dementia research: new frontiers. Alzheimer’s Dement. 15, P1473–P1473 (2019)
McKhann, G.M., et al.: The diagnosis of dementia due to alzheimer’s disease: Recommendations from the national institute on aging-alzheimer’s association workgroups on diagnostic guidelines for alzheimer’s disease. Alzheimer’s Dement. 7(3), 263–269 (2011)
Jack, C.R., et al.: Medial temporal atrophy on MRI in normal aging and very mild alzheimer’s disease. Neurology 49(3), 786–794 (1997)
Nordberg, A.: Pet imaging of amyloid in alzheimer’s disease. Lancet Neurol. 3(9), 519–527 (2004)
Zeng, D., Wang, S., Shen, Y., Shi, C.: A GA-based feature selection and parameter optimization for support tucker machine. In: Procedia computer science, 8th International Conference on Advances in Information Technology, vol. 111, pp. 17–23 (2017)
Wang, S., Hu, Y., Shen, Y., Li, H.: Classification of diffusion tensor metrics for the diagnosis of a myelopathic cord using machine learning. Int. J. Neural Syst. 28(02), 1750036 (2018)
Wang, S., Chen, Z., You, S., Wang, B., Shen, Y., Lei, B.: Brain stroke lesion segmentation using consistent perception generative adversarial network. Neural Comput. Appl. 34(11), 8657–8669 (2022)
Wang, S., et al.: An ensemble-based densely-connected deep learning system for assessment of skeletal maturity. IEEE Trans. Syst. Man Cybern. Syst. 52(1), 426–437 (2022)
Wang, S., Shen, Y., Zeng, D., Hu, Y.: Bone age assessment using convolutional neural networks. In: 2018 International Conference on Artificial Intelligence and Big Data (ICAIBD), pp. 175–178 (2018)
Wang, S.-Q., Li, X., Cui, J.-L., Li, H.-X., Luk, K.D., Hu, Y.: Prediction of myelopathic level in cervical spondylotic myelopathy using diffusion tensor imaging. J. Magn. Reson. Imaging 41(6), 1682–1688 (2015)
Wang, S., Shen, Y., Chen, W., Xiao, T., Hu, J.: Automatic recognition of Mild cognitive impairment from MRI images using expedited convolutional neural networks. In: Lintas, A., Rovetta, S., Verschure, P.F.M.J., Villa, A.E.P. (eds.) ICANN 2017. LNCS, vol. 10613, pp. 373–380. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68600-4_43
Lei, B., Liang, E., Yang, M., Yang, P., Zhou, F., Tan, E.-L., Lei, Y., Liu, C.-M., Wang, T., Xiao, X., et al.: Predicting clinical scores for alzheimer’s disease based on joint and deep learning. Exp. Syst. Appl. 187, 115966 (2022)
Wang, S., Wang, H., Shen, Y., Wang, X.: Automatic recognition of mild cognitive impairment and alzheimers disease using ensemble based 3d densely connected convolutional networks. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 517–523 (2018)
Goodfellow, I.J., et al.: Generative adversarial nets. In: NIPS (2014)
Mo, L., Wang, S.-Q.: A variational approach to nonlinear two-point boundary value problems. Nonlinear Anal. Theor. Methods Appl. 71(12), 834–838 (2009)
Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Adv. Neural Inf. Process. Syst. 33, 6840–6851 (2020)
Yang, H., Qian, P., Fan, C.: An indirect multimodal image registration and completion method guided by image synthesis. Comput. Math. Methods Med. 2020 (2020). Article ID 2684851
Kong, L., Lian, C., Huang, D., Hu, Y., Zhou, Q., et al.: Breaking the dilemma of medical image-to-image translation. Adv. Neural Inf. Process. Syst. 34, 1964–1978 (2021)
Kim, B., Han, I., Ye, J.C.: DiffuseMorph: unsupervised deformable image registration using diffusion model. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds.) Computer Vision - ECCV 2022. ECCV 2022. LNCS, vol. 13691, pp. 347–364. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-19821-2_20
Song, T.-A., Chowdhury, S.R., Yang, F., Dutta, J.: Pet image super-resolution using generative adversarial networks. Neural Netw. 125, 83–91 (2020)
You, S., et al.: Fine perceptive GANs for brain MR image super-resolution in wavelet domain. IEEE Trans. Neural Netw. Learn. Syst. (2022). https://doi.org/10.1109/TNNLS.2022.3153088
Ding, Y., et al.: Tostagan: an end-to-end two-stage generative adversarial network for brain tumor segmentation. Neurocomputing 462, 141–153 (2021)
Wolleb, J., Sandkühler, R., Bieder, F., Valmaggia, P., Cattin, P.C.: Diffusion models for implicit image segmentation ensembles, arXiv preprint arXiv: Arxiv-2112.03145 (2021)
Pinaya, W.H.L., et al.: Fast unsupervised brain anomaly detection and segmentation with diffusion models. In: MICCAI (2022)
Hu, S., Yuan, J., Wang, S.: Cross-modality synthesis from MRI to pet using adversarial u-net with different normalization. In: 2019 International Conference on Medical Imaging Physics and Engineering (ICMIPE), pp. 1–5 (2019)
Hu, S., Lei, B., Wang, S., Wang, Y., Feng, Z., Shen, Y.: Bidirectional mapping generative adversarial networks for brain MR to pet synthesis. IEEE Trans. Med. Imaging 41(1), 145–157 (2021)
Conte, G.M., et al.: Generative adversarial networks to synthesize missing t1 and flair MRI sequences for use in a multisequence brain tumor segmentation model. Radiology 299(2), 313–323 (2021)
Yu, W., Lei, B., Ng, M.K., Cheung, A.C., Shen, Y., Wang, S.: Tensorizing GAN with high-order pooling for alzheimer’s disease assessment. IEEE Trans. Neural Netw. Learn. Syst. 33(9), 4945–4959 (2021)
Yu, W., et al.: Morphological feature visualization of alzheimer’s disease via multidirectional perception GAN. IEEE Trans. Neural Netw. Learn. Syst. 34, 4401–4415 (2022)
Wolleb, J., Bieder, F., Sandkühler, R., Cattin, P.C.: Diffusion models for medical anomaly detection. In: MICCAI (2022)
Chlap, P., Min, H., Vandenberg, N., Dowling, J., Holloway, L., Haworth, A.: A review of medical image data augmentation techniques for deep learning applications. J. Med. Imaging Radiat. Oncol. 65(5), 545–563 (2021)
Hu, S., Yu, W., Chen, Z., Wang, S.: Medical image reconstruction using generative adversarial network for alzheimer disease assessment with class-imbalance problem. In: 2020 IEEE 6th International Conference on Computer and Communications (ICCC), pp. 1323–1327 (2020)
Shaul, R., David, I., Shitrit, O., Raviv, T.R.: Subsampled brain MRI reconstruction by generative adversarial neural networks. Med. Image Anal. 65, 101747 (2020)
Dar, S.U., Yurt, M., Karacan, L., Erdem, A., Erdem, E., Çukur, T.: Image synthesis in multi-contrast MRI with conditional generative adversarial networks. IEEE Trans. Med. Imaging 38(10), 2375–2388 (2019)
Luo, Y., et al.: Edge-preserving MRI image synthesis via adversarial network with iterative multi-scale fusion. Neurocomputing 452, 63–77 (2021)
Akrout, M., et al.: Diffusion-based data augmentation for skin disease classification: impact across original medical datasets to fully synthetic images, arXiv preprint arXiv:2301.04802 (2023)
Pinaya, W.H.L. et al. Brain imaging generation with latent diffusion models. In: Mukhopadhyay, A., Oksuz, I., Engelhardt, S., Zhu, D., Yuan, Y. (eds.) Deep Generative Models. DGM4MICCAI 2022. LNCS, vol. 13609, pp. 117–126. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-18576-2_12
Peng, W., Adeli, E., Zhao, Q., Pohl, K.M.: Generating realistic 3d brain MRIS using a conditional diffusion probabilistic model, arXiv preprint arXiv: Arxiv-2212.08034 (2022)
Mirza, M., Osindero, S.: Conditional generative adversarial nets, arXiv preprint arXiv: Arxiv-1411.1784 (2014)
Odena, A., Olah, C., Shlens, J.: Conditional image synthesis with auxiliary classifier GANs. In: International Conference on Machine Learning (2016)
Acknowledgment
This work was supported by the National Natural Science Foundations of China under Grant 62172403, the Distinguished Young Scholars Fund of Guangdong under Grant 2021B1515020019, the Excellent Young Scholars of Shenzhen under Grant RCYX20200714114641211 and Shenzhen Key Basic Research Project under Grant JCYJ20200109115641762.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Yao, W., Shen, Y., Nicolls, F., Wang, SQ. (2023). Conditional Diffusion Model-Based Data Augmentation for Alzheimer’s Prediction. In: Zhang, H., et al. International Conference on Neural Computing for Advanced Applications. NCAA 2023. Communications in Computer and Information Science, vol 1869. Springer, Singapore. https://doi.org/10.1007/978-981-99-5844-3_3
Download citation
DOI: https://doi.org/10.1007/978-981-99-5844-3_3
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-5843-6
Online ISBN: 978-981-99-5844-3
eBook Packages: Computer ScienceComputer Science (R0)