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一个人类红细胞全息图数据集,用于基于学习的模型培训
Raul Castañeda1, Carlos Trujillo1, Ana Doblas2
1Applied Optics Group, School of Applied Sciences and Engineering EAFIT University, Medellin 050037, Colombia.
Data in brief
|May 6, 2024
概括
这项研究引入了大量人类红细胞全息图的数据集,用于训练人工智能模型. 这使得数字全息显微镜的定量相位成像能够得到改进.
科学领域:
- 生物医学光学 生物医学光学
- 计算成像技术的成像
- 显微镜的使用方法
背景情况:
- 数字全息显微镜 (DHM) 能够进行定量相位成像.
- 精确的相位重建对于生物样本分析至关重要.
- 现有的数据集可能无法完全支持基于学习的高级重建方法.
研究的目的:
- 呈现人体红细胞 (RBC) 全息图及其重建的相位图的综合数据集.
- 促进基于学习的模型的开发和验证,用于无偏差的相位成像.
- 支持对远程和非远程 DHM 系统的研究.
主要方法:
- 使用离轴远程中心DHM系统对全息图的实验记录.
- 使用 40 × /0.65NA 显微镜镜头和 200 毫米管镜头.
- 将300个全息图的初始数据集增加到36864个实例.
主要成果:
- 为人类红细胞建立了一组原始全息图和重建的相位图的配对数据集.
- 该数据集支持对量化相位成像端到端模型的培训和测试.
- 该DHM系统通过其离轴,远中心和衍射有限的设计,确保准确的定量相位图.
结论:
- 这一数据集是推动数字全息显微镜和计算成像技术进步的宝贵资源.
- 它可以对基于学习的模型进行可靠的调查,以便从DHM数据中重建相位图像.
- 这项工作促进了生物应用量的相位成像技术的进步.
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