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

• URL: http://arxiv.org/abs/2505.14754v2
• Date: Mon, 04 Aug 2025 11:42:09 GMT
• Title: Model-Independent Machine Learning Approach for Nanometric Axial Localization and Tracking
• Authors: Andrey Alexandrov, Giovanni Acampora, Giovanni De Lellis, Antonia Di Crescenzo, Chiara Errico, Daria Morozova, Valeri Tioukov, Autilia Vittiello,
• Abstract summary: We introduce a deep learning approach using convolutional neural networks (CNNs) that can determine axial coordinates from dual-focal-plane images.<n>Our method achieves an axial localization precision of 40 nanometers-six times better than traditional single-focal-plane techniques.
• Score: 2.612019169899311
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Deep Learning for Optical Misalignment Diagnostics in Multi-Lens Imaging Systems [0.0]
  We present two complementary deep learning-based inverse-design methods for diagnosing misalignments in multi-element lens systems.<n>First, we use ray-traced spot diagrams to predict five-degree-of-freedom (5-DOF) errors in a 6-lens photographic prime, achieving a mean absolute error of 0.031mm in lateral translation and 0.011$circ$ in tilt.<n>We also introduce a physics-based simulation pipeline that utilizes grayscale synthetic camera images, enabling a deep learning model to estimate 4-DOF, decenter and tilt errors in both two- and six-lens multi-lens systems.
  arXiv  Detail & Related papers  (2025-06-29T10:13:40Z)
• Learning-Based Distance Estimation for 360° Single-Sensor Setups [11.532574301455854]
  We propose a neural network-based approach for monocular distance estimation using a single 360deg fisheye lens camera.<n>Unlike classical trigonometric techniques that rely on precise lens calibration, our method directly learns and infers the distance of objects from raw omnidirectional inputs.
  arXiv  Detail & Related papers  (2025-06-25T16:26:55Z)
• Self-Supervised Z-Slice Augmentation for 3D Bio-Imaging via Knowledge Distillation [65.46249968484794]
  ZAugNet is a fast, accurate, and self-supervised deep learning method for enhancing z-resolution in biological images.<n>By performing nonlinear distances between consecutive slices, ZAugNet effectively doubles resolution with each iteration.<n>ZAugNet+ is an extended version enabling continuous prediction at arbitrary distances.
  arXiv  Detail & Related papers  (2025-03-05T17:50:35Z)
• From Stars to Molecules: AI Guided Device-Agnostic Super-Resolution Imaging [0.0]
  Super-resolution imaging has revolutionized the study of systems ranging from molecular structures to distant galaxies.<n>Existing super-resolution methods require extensive calibration and retraining for each imaging setup, limiting their practical deployment.<n>We introduce a device-agnostic deep-learning framework for super-resolution imaging of point-like emitters that eliminates the need for calibration data or explicit knowledge of optical system parameters.
  arXiv  Detail & Related papers  (2025-02-25T20:54:27Z)
• MaskTerial: A Foundation Model for Automated 2D Material Flake Detection [48.73213960205105]
  We present a deep learning model, called MaskTerial, that uses an instance segmentation network to reliably identify 2D material flakes.<n>The model is extensively pre-trained using a synthetic data generator, that generates realistic microscopy images from unlabeled data.<n>We demonstrate significant improvements over existing techniques in the detection of low-contrast materials such as hexagonal boron nitride.
  arXiv  Detail & Related papers  (2024-12-12T15:01:39Z)
• Toward Efficient Visual Gyroscopes: Spherical Moments, Harmonics Filtering, and Masking Techniques for Spherical Camera Applications [83.8743080143778]
  A visual gyroscope estimates camera rotation through images. The integration of omnidirectional cameras, offering a larger field of view compared to traditional RGB cameras, has proven to yield more accurate and robust results. Here, we address these challenges by introducing a novel visual gyroscope, which combines an Efficient Multi-Mask-Filter Rotation Estor and a Learning based optimization.
  arXiv  Detail & Related papers  (2024-04-02T13:19:06Z)
• Machine learning-based spin structure detection [0.0]
  We report a convolutional neural network specifically designed for segmentation problems to detect the position and shape of skyrmions in our measurements. The results of this study show that a well-trained network is a viable method of automating data pre-processing in magnetic microscopy.
  arXiv  Detail & Related papers  (2023-03-24T17:19:31Z)
• ChemVise: Maximizing Out-of-Distribution Chemical Detection with the Novel Application of Zero-Shot Learning [60.02503434201552]
  This research proposes learning approximations of complex exposures from training sets of simple ones. We demonstrate this approach to synthetic sensor responses surprisingly improves the detection of out-of-distribution obscured chemical analytes.
  arXiv  Detail & Related papers  (2023-02-09T20:19:57Z)
• Deep Learning for Real Time Satellite Pose Estimation on Low Power Edge TPU [58.720142291102135]
  In this paper we propose a pose estimation software exploiting neural network architectures. We show how low power machine learning accelerators could enable Artificial Intelligence exploitation in space.
  arXiv  Detail & Related papers  (2022-04-07T08:53:18Z)
• Tracking perovskite crystallization via deep learning-based feature detection on 2D X-ray scattering data [137.47124933818066]
  We propose an automated pipeline for the analysis of X-ray diffraction images based on the Faster R-CNN deep learning architecture. We demonstrate our method on real-time tracking of organic-inorganic perovskite structure crystallization and test it on two applications.
  arXiv  Detail & Related papers  (2022-02-22T15:39:00Z)
• Ultrafast Focus Detection for Automated Microscopy [0.0]
  We present a fast out-of-focus detection algorithm for electron microscopy images collected serially. Our technique, Multi-scale Histologic Feature Detection, adapts classical computer vision techniques and is based on detecting various fine-grained histologic features. Tests are performed that demonstrate near-real-time detection of out-of-focus conditions.
  arXiv  Detail & Related papers  (2021-08-26T22:24:41Z)
• A parameter refinement method for Ptychography based on Deep Learning concepts [55.41644538483948]
  coarse parametrisation in propagation distance, position errors and partial coherence frequently menaces the experiment viability. A modern Deep Learning framework is used to correct autonomously the setup incoherences, thus improving the quality of a ptychography reconstruction. We tested our system on both synthetic datasets and also on real data acquired at the TwinMic beamline of the Elettra synchrotron facility.
  arXiv  Detail & Related papers  (2021-05-18T10:15:17Z)
• Many-to-One Distribution Learning and K-Nearest Neighbor Smoothing for Thoracic Disease Identification [83.6017225363714]
  deep learning has become the most powerful computer-aided diagnosis technology for improving disease identification performance. For chest X-ray imaging, annotating large-scale data requires professional domain knowledge and is time-consuming. In this paper, we propose many-to-one distribution learning (MODL) and K-nearest neighbor smoothing (KNNS) methods to improve a single model's disease identification performance.
  arXiv  Detail & Related papers  (2021-02-26T02:29:30Z)
• Synthetic Image Rendering Solves Annotation Problem in Deep Learning Nanoparticle Segmentation [5.927116192179681]
  We show that using a rendering software allows to generate realistic, synthetic training data to train a state-of-the art deep neural network. We derive a segmentation accuracy that is comparable to man-made annotations for toxicologically relevant metal-oxide nanoparticles ensembles.
  arXiv  Detail & Related papers  (2020-11-20T17:05:36Z)
• Single-molecule orientation localization microscopy I: fundamental limits [0.0]
  We adapt classical and quantum estimation theory and propose a mathematical framework to derive the best possible precision. We find that it is impossible to design an instrument that achieves the maximum sensitivity limit for measuring all possible rotational motions. Overall, we conclude that no single instrument can be optimized for maximum precision across all possible 2D and 3D localization and orientation measurement tasks.
  arXiv  Detail & Related papers  (2020-10-08T15:27:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.