Significance: Digital holographic microscopy (DHM) is a promising technique for the study of semitransparent biological specimen such as red blood cells (RBCs). It is important and meaningful to detect and count biological cells at the single cell level in biomedical images for biomarker discovery and disease diagnostics. However, the biological cell analysis based on phase information of images is inefficient due to the complexity of numerical phase reconstruction algorithm applied to raw hologram images. New cell study methods based on diffraction pattern directly are desirable. Aim: Deep fully convolutional networks (FCNs) were developed on raw hologram images directly for high-throughput label-free cell detection and counting to assist the biological cell analysis in the future. Approach: The raw diffraction patterns of RBCs were recorded by use of DHM. Ground-truth mask images were labeled based on phase images reconstructed from RBC holograms using numerical reconstruction algorithm. A deep FCN, which is UNet, was trained on the diffraction pattern images to achieve the label-free cell detection and counting. Results: The implemented deep FCNs provide a promising way to high-throughput and label-free counting of RBCs with a counting accuracy of 99% at a throughput rate of greater than 288 cells per second and 200 μm × 200 μm field of view at the single cell level. Compared to convolutional neural networks, the FCNs can get much better results in terms of accuracy and throughput rate. Conclusions: High-throughput label-free cell detection and counting were successfully achieved from diffraction patterns with deep FCNs. It is a promising approach for biological specimen analysis based on raw hologram directly.

中文翻译：

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使用深度全卷积神经网络从衍射图案中进行高通量无标记细胞检测和计数

意义：数字全息显微镜（DHM）是一种用于研究半透明生物标本如红细胞（RBCs）的有前途的技术。在生物医学图像中在单细胞水平上检测和计数生物细胞对于生物标志物的发现和疾病诊断具有重要意义。然而，由于应用于原始全息图像的数值相位重建算法的复杂性，基于图像相位信息的生物细胞分析效率低下。直接基于衍射图案的新细胞研究方法是可取的。目的：直接在原始全息图像上开发深度全卷积网络 (FCN)，用于高通量无标记细胞检测和计数，以协助未来的生物细胞分析。方法：使用 DHM 记录 RBC 的原始衍射图。基于使用数值重建算法从 RBC 全息图重建的相位图像，对真实掩码图像进行标记。在衍射图案图像上训练了一个深度 FCN，即 UNet，以实现无标记细胞检测和计数。结果：实施的深度 FCN 为高通量和无标记的红细胞计数提供了一种有前景的方法，在每秒大于 288 个细胞的吞吐率和 200 μm × 200 μm 的视野范围内，计数准确率为 99%。单细胞水平。与卷积神经网络相比，FCNs 在准确性和吞吐率方面可以获得更好的结果。结论：从具有深 FCN 的衍射图案成功实现了高通量无标记细胞检测和计数。