Deep Learning Enhanced Mobile-Phone Microscopy,ACS Photonics

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Deep Learning Enhanced Mobile-Phone Microscopy
ACS Photonics ( IF 6.5 ) Pub Date : 2018-03-15 00:00:00 , DOI: 10.1021/acsphotonics.8b00146
Yair Rivenson _{1,

2,

3} , Hatice Ceylan Koydemir _{1,

2,

3} , Hongda Wang _{1,

2,

3} , Zhensong Wei ₁ , Zhengshuang Ren ₁ , Harun Günaydın ₁ , Yibo Zhang _{1,

2,

3} , Zoltán Göröcs _{1,

2,

3} , Kyle Liang ₁ , Derek Tseng ₁ , Aydogan Ozcan _{1,

2,

3,

4}

Affiliation

Mobile phones have facilitated the creation of field-portable, cost-effective imaging and sensing technologies that approach laboratory-grade instrument performance. However, the optical imaging interfaces of mobile phones are not designed for microscopy and produce distortions in imaging microscopic specimens. Here, we report on the use of deep learning to correct such distortions introduced by mobile-phone-based microscopes, facilitating the production of high-resolution, denoised, and color-corrected images, matching the performance of benchtop microscopes with high-end objective lenses, also extending their limited depth of field. After training a convolutional neural network, we successfully imaged various samples, including human tissue sections and Papanicolaou and blood smears, where the recorded images were highly compressed to ease storage and transmission. This method is applicable to other low-cost, aberrated imaging systems and could offer alternatives for costly and bulky microscopes, while also providing a framework for standardization of optical images for clinical and biomedical applications.

中文翻译：

深度学习增强型手机显微镜

移动电话促进了接近实验室级仪器性能的现场便携式，经济高效的成像和传感技术的创建。然而，移动电话的光学成像接口不是为显微镜设计的，并且会在成像显微样本中产生畸变。在这里，我们报告了深度学习的使用，以纠正基于移动电话的显微镜引入的这种畸变，从而有助于生成高分辨率，去噪和色彩校正的图像，使台式显微镜的性能与高端物镜相匹配。镜头，也扩大了其有限的景深。训练了卷积神经网络后，我们成功拍摄了各种样本的图像，包括人体组织切片，巴氏和血涂片，记录的图像经过高度压缩以简化存储和传输的位置。该方法适用于其他低成本，像差成像系统，并且可以为昂贵且笨重的显微镜提供替代方案，同时还为临床和生物医学应用提供了光学图像标准化的框架。

更新日期：2018-03-15

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