期刊
SENSORS AND ACTUATORS A-PHYSICAL
卷 331, 期 -, 页码 -出版社
ELSEVIER SCIENCE SA
DOI: 10.1016/j.sna.2021.112928
关键词
Deep learning; Artificial intelligence; Mini-microscopy; Optical imaging; Biomedicine
资金
- University of Massachusetts Dartmouth College of Engineering faculty start-up funding
- Brigham Research Institute
- National Institutes of Health [R03EB027984, R01EB028143]
This paper optimizes artificial intelligence techniques to provide clear and natural biomedical imaging, demonstrating the significant improvements in spatial resolution of mini-microscopy and regular-microscopy achieved through a deep learning-enabled super-resolution method. Qualitative and quantitative comparisons reveal the advantages of the deep learning approach in enhancing image quality.
Artificial intelligence algorithms that aid mini-microscope imaging are attractive for numerous applications. In this paper, we optimize artificial intelligence techniques to provide clear, and natural biomedical imaging. We demonstrate that a deep learning-enabled super-resolution method can significantly enhance the spatial resolution of mini-microscopy and regular-microscopy. This data-driven approach trains a generative adversarial network to transform low-resolution images into super-resolved ones. Mini-microscopic images and regular-microscopic images acquired with different optical microscopes under various magnifications are collected as our experimental benchmark datasets. The only input to this generative-adversarial-network-based method are images from the datasets down-sampled by the Bicubic interpolation. We use independent test sets to evaluate this deep learning approach with other deep learning-based algorithms through qualitative and quantitative comparisons. To clearly present the improvements achieved by this generative-adversarial-network-based method, we zoom into the local features to explore and highlight the qualitative differences. We also employ the peak signal-to-noise ratio and the structural similarity, to quantitatively compare alternative super-resolution methods. The quantitative results illustrate that super-resolution images obtained from our approach with interpolation parameter alpha = 0.25 more closely match those of the original high-resolution images than to those obtained by any of the alternative state-of-the-art method. These results are significant for fields that use microscopy tools, such as biomedical imaging of engineered living systems. We also utilize this generative adversarial network-based algorithm to optimize the resolution of biomedical specimen images and then generate three-dimensional reconstruction, so as to enhance the ability of three-dimensional imaging throughout the entire volumes for spatial-temporal analyses of specimen structures. (C) 2021 Elsevier B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据