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摘要翻译：
利用深度神经网络，我们展示了一种数字染色技术，我们称之为相位，将无标记组织切片的定量相位图像(QPI)转换成与组织化学染色的相同样本的brightfield显微镜图像等效的图像。通过对图像数据（染色后获得的QPI和相应的brightfield图像）训练生成对抗网络(GAN)，并利用人体皮肤、肾脏和肝脏组织切片，分别与苏木精和伊红染色、Jones染色和Masson三色染色的相同样本的brightfield显微镜图像进行匹配，证明了这种虚拟染色方法的有效性。该数字染色框架可进一步加强无标记QPI技术在病理学应用和一般生物医学研究中的各种应用，消除对化学染色的需要，降低样品制备的相关成本和节省时间。我们的结果提供了一个强有力的例子，说明由深度学习支持的数据驱动图像转换所创造的一些独特机会。
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英文标题：
《PhaseStain: Digital staining of label-free quantitative phase microscopy
images using deep learning》
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作者：
Yair Rivenson, Tairan Liu, Zhensong Wei, Yibo Zhang, Aydogan Ozcan
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最新提交年份：
2018
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分类信息：
一级分类：Electrical Engineering and Systems Science 电气工程与系统科学
二级分类：Image and Video Processing 图像和视频处理
分类描述：Theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications. Topics of interest include: mathematical, statistical, and perceptual image and video modeling and representation; linear and nonlinear filtering, de-blurring, enhancement, restoration, and reconstruction from degraded, low-resolution or tomographic data; lossless and lossy compression and coding; segmentation, alignment, and recognition; image rendering, visualization, and printing; computational imaging, including ultrasound, tomographic and magnetic resonance imaging; and image and video analysis, synthesis, storage, search and retrieval.
用于图像、视频和多维信号的形成、捕获、处理、通信、分析和显示的理论、算法和体系结构。感兴趣的主题包括：数学，统计，和感知图像和视频建模和表示；线性和非线性滤波、去模糊、增强、恢复和重建退化、低分辨率或层析数据；无损和有损压缩编码；分割、对齐和识别；图像渲染、可视化和打印；计算成像，包括超声、断层和磁共振成像；以及图像和视频的分析、合成、存储、搜索和检索。
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一级分类：Computer Science 计算机科学
二级分类：Computer Vision and Pattern Recognition 计算机视觉与模式识别
分类描述：Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.
涵盖图像处理、计算机视觉、模式识别和场景理解。大致包括ACM课程I.2.10、I.4和I.5中的材料。
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一级分类：Physics 物理学
二级分类：Medical Physics 医学物理学
分类描述：Radiation therapy. Radiation dosimetry. Biomedical imaging modelling. Reconstruction, processing, and analysis. Biomedical system modelling and analysis. Health physics. New imaging or therapy modalities.
放射治疗。辐射剂量学。生物医学成像建模。重建、处理和分析。生物医学系统建模与分析。健康物理学。新的成像或治疗方式。
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英文摘要：
Using a deep neural network, we demonstrate a digital staining technique, which we term PhaseStain, to transform quantitative phase images (QPI) of labelfree tissue sections into images that are equivalent to brightfield microscopy images of the same samples that are histochemically stained. Through pairs of image data (QPI and the corresponding brightfield images, acquired after staining) we train a generative adversarial network (GAN) and demonstrate the effectiveness of this virtual staining approach using sections of human skin, kidney and liver tissue, matching the brightfield microscopy images of the same samples stained with Hematoxylin and Eosin, Jones' stain, and Masson's trichrome stain, respectively. This digital staining framework might further strengthen various uses of labelfree QPI techniques in pathology applications and biomedical research in general, by eliminating the need for chemical staining, reducing sample preparation related costs and saving time. Our results provide a powerful example of some of the unique opportunities created by data driven image transformations enabled by deep learning.
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PDF链接：
https://arxiv.org/pdf/1807.07701
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