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深度学习使得随机光学重建显微镜类似的超分辨率图像从常规显微镜的重建

Lei Xu^1,2, Shichao Kan³, Xiying Yu¹

¹Department of Etiology and Carcinogenesis and State Key Laboratory of Molecular Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.

|October 23, 2023

概括

X-Microscopy使用深度学习从广场图像中实现超分辨率显微镜. 这种计算工具提高了图像质量,使先进显微镜更容易获得.

科学领域:

生物物理学的生物物理.
计算生物学计算生物学
显微镜的使用方法

背景情况:

深度学习显示出超高分辨率显微镜的前景.
从传统的广场显微镜实现高质量的超级分辨率存在挑战.

研究的目的:

从广场图像开发超分辨率显微镜图像重建的计算工具.
为了实现类似STORM的超高分辨率成像,并具有输入大小的灵活性.

主要方法:

开发了X-Microscopy,这是一个具有两个深度学习子网络的工具:UR-Net-8和X-Net.
训练了各种亚细胞结构 (细胞骨纤维,纳米集群等) 的模型. ) 的情况.

主要成果:

X显微镜能够从宽场显微镜进行类似STORM的超高分辨率图像重建.
实现了与STORM类图像相提并论的高质量重建.
促进多色和多系统超高分辨率成像.

结论:

X显微镜增强了超高分辨率显微镜的能力.
该工具使得超高分辨率成像的访问超出了专业实验室的范围.

关键词:

机器学习机器学习医学成像医学成像光学成像技术的使用.

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