Model-Independent Machine Learning Approach for Nanometric Axial Localization and Tracking

2025-05-20

AI Summary

Axial localization accuracy of nanoparticles in optical microscopy is fundamentally limited by modeling errors and complexity inherent in conventional physical models (e.g., point spread function). To address this, we propose a model-agnostic, lightweight convolutional neural network (CNN) for end-to-end regression that directly estimates the axial position of a particle from a pair of defocused bright-field or dark-field images acquired at two distinct focal planes. By eliminating reliance on prior physical assumptions, the method achieves superior generalizability and real-time inference capability. Experimentally, it attains a 40 nm axial localization precision—sixfold improvement over single-plane approaches. The framework has been successfully deployed across diverse applications, including dark matter detection, proton therapy, space radiation shielding, and biological imaging, demonstrating high accuracy, robustness to experimental variability, and broad cross-domain applicability.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Computer Vision: Medical and Biological Imaging
• Search and Optimization: Non-convex Optimization

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Accurately tracking particles and determining their position 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 positions from dual-focal plane images without relying on predefined models. Our method achieves an axial localization accuracy 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.

Problem

• Accurately tracking nanometric particle positions in microscopy
• Overcoming limitations of traditional single-focal plane techniques
• Enabling precise axial localization without predefined models

Innovation

• Deep learning with CNNs for axial localization
• Dual-focal plane images without predefined models
• 40nm accuracy, six times better than traditional