用深度学习从双焦平面图像实现纳米级轴向定位，精度达40纳米。
Model-Independent Machine Learning Approach for Nanometric Axial Localization and Tracking
- 基于CNN的无模型方法，直接从双焦平面图像推断轴向位置。
- 轴向定位精度达40纳米，较传统单焦面技术提升6倍。
- 适用于生物成像、粒子追踪等多领域，部署灵活。
在光学显微镜中，精确追踪粒子并确定其沿光轴的位置是重大挑战，尤其在需要极高精度时。本文提出一种基于卷积神经网络（CNN）的无模型深度学习方法，可仅凭双焦平面图像实现轴向坐标确定。该方法实现了40纳米的轴向定位精度，比传统单焦面技术提高六倍。模型设计简洁，性能优异，适用于暗物质探测、癌症质子治疗及太空辐射防护等多种场景，也展现出在生物成像、材料科学和环境监测中的潜力。本研究展示了机器学习如何将复杂图像数据转化为高可靠性的精准信息，为多种科学应用提供了一种灵活且强大的工具。
原文摘要 · Abstract (English)
Accurately tracking particles and determining their coordinate along the optical axis is a major challenge in optical microscopy, especially when extremely high precision is needed. In this study, we introduce a deep learning approach using convolutional neural networks (CNNs) that can determine axial coordinates from dual-focal-plane images without relying on predefined models. Our method achieves an axial localization precision of 40 nanometers-six times better than traditional single-focal-plane techniques. The model's simple design and strong performance make it suitable for a wide range of uses, including dark matter detection, proton therapy for cancer, and radiation protection in space. It also shows promise in fields like biological imaging, materials science, and environmental monitoring. This work highlights how machine learning can turn complex image data into reliable, precise information, offering a flexible and powerful tool for many scientific applications.
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