https://scholars.lib.ntu.edu.tw/handle/123456789/639755
標題: | Isotropic multi-scale neuronal reconstruction from high-ratio expansion microscopy with contrastive unsupervised deep generative models | 作者: | Chang, Gary Han Wu, Meng Yun Yen, Ling Hui Huang, Da Yu Lin, Ya Hui Luo, Yi Ru Liu, Ya Ding Xu, Bin Leong, Kam W. WEN-SUNG LAI Chiang, Ann Shyn Wang, Kuo Chuan CHIN-HSIEN LIN Wang, Shih Luen Chu, Li An |
關鍵字: | Biomedical imaging | Deep learning | Expansion microscopy | 公開日期: | 1-二月-2024 | 卷: | 244 | 來源出版物: | Computer Methods and Programs in Biomedicine | 摘要: | Background and objective: Current methods for imaging reconstruction from high-ratio expansion microscopy (ExM) data are limited by anisotropic optical resolution and the requirement for extensive manual annotation, creating a significant bottleneck in the analysis of complex neuronal structures. Methods: We devised an innovative approach called the IsoGAN model, which utilizes a contrastive unsupervised generative adversarial network to sidestep these constraints. This model leverages multi-scale and isotropic neuron/protein/blood vessel morphology data to generate high-fidelity 3D representations of these structures, eliminating the need for rigorous manual annotation and supervision. The IsoGAN model introduces simplified structures with idealized morphologies as shape priors to ensure high consistency in the generated neuronal profiles across all points in space and scalability for arbitrarily large volumes. Results: The efficacy of the IsoGAN model in accurately reconstructing complex neuronal structures was quantitatively assessed by examining the consistency between the axial and lateral views and identifying a reduction in erroneous imaging artifacts. The IsoGAN model accurately reconstructed complex neuronal structures, as evidenced by the consistency between the axial and lateral views and a reduction in erroneous imaging artifacts, and can be further applied to various biological samples. Conclusion: With its ability to generate detailed 3D neurons/proteins/blood vessel structures using significantly fewer axial view images, IsoGAN can streamline the process of imaging reconstruction while maintaining the necessary detail, offering a transformative solution to the existing limitations in high-throughput morphology analysis across different structures. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/639755 | ISSN: | 01692607 | DOI: | 10.1016/j.cmpb.2023.107991 |
顯示於: | 心理學系 |
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