Abstract
Brain network analysis is essential for diagnosing and intervention for Alzheimer’s disease (AD). However, previous research relied primarily on specific time-consuming and subjective toolkits. Only few tools can obtain the structural brain networks from brain diffusion tensor images (DTI). In this paper, we propose a diffusion based end-to-end brain network generative model Brain Diffuser that directly shapes the structural brain networks from DTI. Compared to existing toolkits, Brain Diffuser exploits more structural connectivity features and disease-related information by analyzing disparities in structural brain networks across subjects. For the case of Alzheimer’s disease, the proposed model performs better than the results from existing toolkits on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database.
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Acknowledgement
This work is supported in part by the National Natural Science Foundations of China under Grant 62172403, in part by the Distinguished Young Scholars Fund of Guangdong under Grant 2021B1515020019, in part by the Excellent Young Scholars of Shenzhen under Grant RCYX20200714114641211 and in part by Shenzhen Key Basic Research Project under Grant JCYJ20200109115641762, in part by the University of Macau under Grant MYRG2022-00190-FST, in part by the Science and Technology Development Fund, Macau SAR, under Grant 0034/2019/AMJ and Grant 0087/2020/A2.
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Chen, X., Lei, B., Pun, CM., Wang, S. (2024). Brain Diffuser: An End-to-End Brain Image to Brain Network Pipeline. In: Liu, Q., et al. Pattern Recognition and Computer Vision. PRCV 2023. Lecture Notes in Computer Science, vol 14437. Springer, Singapore. https://doi.org/10.1007/978-981-99-8558-6_2
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DOI: https://doi.org/10.1007/978-981-99-8558-6_2
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