High-Speed Chemical Imaging by Dense-Net Learning of Femtosecond Stimulated Raman Scattering.
High-Speed Chemical Imaging by Dense-Net Learning of Femtosecond Stimulated Raman Scattering.
复制标题
高速学成像通过密集的网络学习飞秒刺激的拉曼散射。
DOI：
10.1021/acs.jpclett.0c01598
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发表时间：
2020-10-15
影响因子：
5.7
通讯作者：
Cheng, Ji-Xin
中科院分区：
文献类型：
--
作者：
Zhang, Jing;Zhao, Jian;Lin, Haonan;Tan, Yuying;Cheng, Ji-Xin
高光谱受激拉曼散射（SRS）通过对啁啾飞秒脉冲的时间扫描，利用光谱聚焦可以获得无标记的化学图像。然而，脉冲啁啾降低了脉冲峰值功率，时间扫描增加了采集时间，导致成像速度比使用飞秒脉冲的单帧SRS慢得多。在本文中，我们提出了一种深度学习算法来解决从单帧飞秒SRS图像中获得化学标记图像的逆问题。我们基于DenseNet的学习方法，称为DeepChem，可以实现大信号水平的高速化学成像。在MIA PaCa-2细胞和未用于训练的其他细胞类型中分类的四种亚细胞组分（脂滴、内质网、细胞核、细胞质）的情况下，速度提高了2个数量级。脂滴动力学和细胞反应二硫苏糖醇活MIA PaCa-2细胞证明使用这种计算多重方法。
Hyperspectral stimulated Raman scattering (SRS) by spectral focusing can generate label-free chemical images through temporal scanning of chirped femtosecond pulses. Yet, pulse chirping decreases the pulse peak power and temporal scanning increases the acquisition time, resulting in a much slower imaging speed compared to single-frame SRS using femtosecond pulses. In this paper, we present a deep learning algorithm to solve the inverse problem of getting a chemically labeled image from a single-frame femtosecond SRS image. Our DenseNet-based learning method, termed as DeepChem, achieves high-speed chemical imaging with a large signal level. Speed is improved by 2 orders of magnitude with four sub-cellular components (lipid droplet, endoplasmic reticulum, nuclei, cytoplasm) classified in MIA PaCa-2 cells and other cell types which were not used for training. Lipid droplet dynamics and cellular response to Dithiothreitol in live MIA PaCa-2 cells are demonstrated using this computationally multiplex method.
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影响因子：
19.4
作者：
Liao, Chien-Sheng;Slipchenko, Mikhail N.;Cheng, Ji-Xin
通讯作者：
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