深度学习赋能的计算干涉显微技术
Computational interference microscopy enabled by deep learning
AI总结：
本文提出计算干涉显微（CIM）方法，利用基于U-net的深度学习模型从单次曝光DPM图像生成SLIM质量的高灵敏度相位图，去除激光散斑和背景相位噪声，并实现实时低噪声相位成像，在血液涂片上验证了静态和动态成像效果。
摘要：
定量相位成像（QPI）已被广泛应用于细胞和组织的表征。空间光干涉显微镜（SLIM）是一种高灵敏度的QPI方法，这得益于其部分相干照明和共光路干涉几何结构。然而，由于采用四帧相移方案，其采集速率受到限制。另一方面，离轴方法如衍射相位显微镜（DPM）允许单次曝光QPI。但是，基于激光的DPM系统受到散斑和多次反射引起的空间噪声的困扰。在并行发展中，深度学习在生物成像领域被证明具有重要价值，尤其是因为它能够将一种对比度形式转换为另一种对比度形式。在此，我们提出利用深度学习从DPM单次曝光图像作为输入，生成合成的、具有SLIM质量的高灵敏度相位图。我们使用了一台倒置显微镜，其两个端口分别连接到DPM和SLIM模块，从而能够在同一视场上获取两种类型的图像。我们构建了一个基于U-net的深度学习模型，并在超过1000对DPM和SLIM图像上进行了训练。该模型学会了去除激光DPM中的散斑，并在测试集和新数据中克服了背景相位噪声。此外，我们将神经网络推理功能集成到实时采集软件中，现在DPM用户可以实时观察极低噪声的相位图像。我们利用血液涂片展示了这种计算干涉显微（CIM）成像原理，因为血液涂片中同时包含红细胞和白细胞，并在静态和动态条件下进行了验证。
英文摘要：
Quantitative phase imaging (QPI) has been widely applied in characterizing cells and tissues. Spatial light interference microscopy (SLIM) is a highly sensitive QPI method, due to its partially coherent illumination and common path interferometry geometry. However, its acquisition rate is limited because of the four-frame phase-shifting scheme. On the other hand, off-axis methods like diffraction phase microscopy (DPM), allows for single-shot QPI. However, the laser-based DPM system is plagued by spatial noise due to speckles and multiple reflections. In a parallel development, deep learning was proven valuable in the field of bioimaging, especially due to its ability to translate one form of contrast into another. Here, we propose using deep learning to produce synthetic, SLIM-quality, high-sensitivity phase maps from DPM, single-shot images as input. We used an inverted microscope with its two ports connected to the DPM and SLIM modules, such that we have access to the two types of images on the same field of view. We constructed a deep learning model based on U-net and trained on over 1,000 pairs of DPM and SLIM images. The model learned to remove the speckles in laser DPM and overcame the background phase noise in both the test set and new data. Furthermore, we implemented the neural network inference into the live acquisition software, which now allows a DPM user to observe in real-time an extremely low-noise phase image. We demonstrated this principle of computational interference microscopy (CIM) imaging using blood smears, as they contain both erythrocytes and leukocytes, in static and dynamic conditions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
