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Deep learning virtual colorful lens-free on-chip microscopy

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【作者】 沈华高金铭

【Author】 Hua Shen;Jinming Gao;School of Electronic Engineering and Optoelectronic Technology, Nanjing University of Science and Technology;MIIT Key Laboratory of Advanced Solid Laser, Nanjing University of Science and Technology;Department of Material Science and Engineering, University of California Los Angeles;

【通讯作者】 沈华;

【机构】 School of Electronic Engineering and Optoelectronic Technology, Nanjing University of Science and TechnologyMIIT Key Laboratory of Advanced Solid Laser, Nanjing University of Science and TechnologyDepartment of Material Science and Engineering, University of California Los Angeles

【摘要】 Currently, it is generally known that lens-free holographic microscopy, which has no imaging lens, can realize a large field-of-view imaging with a low-cost setup. However, in order to obtain colorful images, traditional lensfree holographic microscopy should utilize at least three quasi-chromatic light sources of discrete wavelengths,such as red LED, green LED, and blue LED. Here, we present a virtual colorization by deep learning methods to transfer a gray lens-free microscopy image into a colorful image. Through pairs of images, i.e., grayscale lens-free microscopy images under green LED at 550 nm illumination and colorful bright-field microscopy images, a generative adversarial network(GAN) is trained, and its effectiveness of virtual colorization is proved by applying it to hematoxylin and eosin stained pathological tissue samples imaging. Our computational virtual colorization method might strengthen the monochromatic illumination lens-free microscopy in medical pathology applications and label staining biomedical research.

【Abstract】 Currently, it is generally known that lens-free holographic microscopy, which has no imaging lens, can realize a large field-of-view imaging with a low-cost setup. However, in order to obtain colorful images, traditional lensfree holographic microscopy should utilize at least three quasi-chromatic light sources of discrete wavelengths,such as red LED, green LED, and blue LED. Here, we present a virtual colorization by deep learning methods to transfer a gray lens-free microscopy image into a colorful image. Through pairs of images, i.e., grayscale lens-free microscopy images under green LED at 550 nm illumination and colorful bright-field microscopy images, a generative adversarial network(GAN) is trained, and its effectiveness of virtual colorization is proved by applying it to hematoxylin and eosin stained pathological tissue samples imaging. Our computational virtual colorization method might strengthen the monochromatic illumination lens-free microscopy in medical pathology applications and label staining biomedical research.

【基金】 partially supported by the National Natural Science Foundation of China (No. 61775096);Fundamental Research Funds for the Central Universities(No. 30919011261);National Key Research and Development Program (No. 2019YFB2005500);Key Laboratory of Optical System Advanced Manufacturing Technology (Chinese Academy of Sciences)(No. KLOMT190101)
  • 【文献出处】 Chinese Optics Letters ,中国光学快报(英文版) , 编辑部邮箱 ,2020年12期
  • 【分类号】TH742;TP18;TP391.41
  • 【被引频次】1
  • 【下载频次】18
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