Awesome-Particle-Vision-Analysis

Particle Vision Analysis (PVA) merges AI-enhanced imaging and computational techniques to study particles across various media, serving as a cornerstone in understanding their complex properties and behaviors. This multidisciplinary field significantly impacts materials science, pharmaceuticals, environmental science, and industrial processing, contributing to the advancement of both technology and science by providing detailed quantitative and qualitative insights into particle size, shape, distribution, composition, and dynamics

Here, we provide a non-exhaustive list of papers that use AI techniques to analyze and characterize particles in various media.

🌟 Introduction

(a), Particle diversity and industry applications: Ranging from nanoparticles (1,nm to 100,nm) like gold and silica crucial in medical therapies and drug delivery, to microparticles (0.1,$\mu$m to 100,$\mu$m) such as polymer spheres and cells important in slow-release medications and biological research. Granules (100,$\mu$m to 2,mm), used in sectors from agriculture to cleaning, and coarse particles (2,mm to 10,mm) such as sand and gravel, are essential in construction and environmental management. (b), Overview of particle vision analysis: This discipline focuses on the detection, segmentation, tracking, classification, and super-resolution imaging of particles. It utilizes advanced algorithms and imaging techniques to analyze particles ranging from microscopic cell structures to larger environmental samples, with applications spanning materials science, pharmaceuticals, environmental science, food and beverage, and aerospace. (c), Scientific discovery and optimization cycle via particle vision analysis: Initiating with exploration tasks like drug discovery, material synthesis, and catalyst design, this cycle progresses through experimentation where observations and data are gathered and analyzed with advanced particle imaging techniques. The feedback loop from these experiments drives further refinements, enhancing insights or optimizing conditions, thus promoting continuous advancement in various fields.

🔧 Tools

Source code and containers for AI-driven particle vision analysis.

Application	Particle Size	Package name and Link	Comments
Particle Segmentation	Microparticles	Cellpose	A generalist algorithm for cell and nucleus segmentation.
Particle Segmentation	Microparticles or Granules	qupath	An open source software for bioimage analysis.
Particle Detection and Segmentation	Microparticles or Granules	stardist	A deep learning nuclei segmentation method originally developed for fluorescence microscopy, can be extended and successfully applied to histopathology images.
Particle Classification and Segmentation	Microparticles or Granules	tiatoolbox	A computational pathology toolbox developed by TIA Centre that provides an end-to-end API for pathology image analysis using best practices.
Particle Segmentation	Microparticles or Granules	micro-sam	A tool for interactive and automatic segmentation and tracking of objects in multi-dimensional microscopy data.
Particle Segmentation	Microparticles or Granules	DeepLIIF	Deep-Learning inferred multiplex immunofluorescence for immunohistochemical image quantification.
Particle Segmentation	Microparticles or Granules	Spateo	Multidimensional spatiotemporal modeling of single-cell spatial transcriptomics.
Particle Segmentation	Microparticles or Granules	MEDIAR	Harmony of Data-Centric and Model-Centric for Multi-Modality Microscopy.
Particle Tracking	Microparticles or Granules	BigNeuron	A resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets.
Particle Tracking	Microparticles or Granules	Usiigaci	An all-in-one, semi-automated pipeline to segment, track, and visualize cell movement and morphological changes in phase contrast microscopy.
Particle Tracking	Microparticles or Granules	TrackMate	An extensible platform where developers can easily write their own detection, particle linking, visualization or analysis algorithms.
Particle Tracking	Microparticles or Granules	DeepTrack2	A comprehensive deep learning framework for digital microscopy.
Particle Tracking	Microparticles or Granules	Bayesian Tracker	A Python library for multi-object tracking, used to reconstruct trajectories in crowded fields.
Super-Resolution	Nanoparticles or Microparticles	PSSR	A Deep Learning-based framework that facilitates otherwise unattainable resolution, speed and sensitivity of point-scanning imaging systems (e.g. scanning electron or laser scanning confocal microscopes).
Super-Resolution	Nanoparticles or Microparticles	CAFI	Deep Learning-based temporal super-resolution for fast bioimaging.
Super-Resolution	Nanoparticles or Microparticles	FD-DeepLoc	Field dependent deep learning enables high-throughput whole-cell 3D super-resolution imaging.
Super-Resolution	Nanoparticles or Microparticles	DeepSTORM3D	Dense 3D localization microscopy and PSF design by deep learning.

📚 Paper

2024

• Application of Artificial Intelligence in Particle and Impurity Detection and Removal: A Survey. (IEEE Access 2024) [paper]

2023

• Geometric deep learning reveals the spatiotemporal features of microscopic motion. (Nature Machine Intelligence 2023) [paper]
• u-track3D: Measuring, navigating, and validating dense particle trajectories in three dimensions. (Cell Reports Methods 2023) [paper]
• Deep Learning Method for Probabilistic Particle Detection and Tracking in Fluorescence Microscopy Images. (IEEE 20th International Symposium on Biomedical Imaging (ISBI) 2023) [paper]
• A Motion Transformer for Single Particle Tracking in Fluorescence Microscopy Images. (Medical Image Computing and Computer Assisted Intervention 2023) [paper]

2022

• Nanoparticle Detection on SEM Images Using a Neural Network and Semi-Synthetic Training Data. (Nanomaterials (Basel, Switzerland) 2022) [paper]
• Deep learning detection of nanoparticles and multiple object tracking of their dynamic evolution during in situ ETEM studies. (Scientific Reports 2022) [paper]
• Particle recognition on transmission electron microscopy images using computer vision and deep learning for catalytic applications. (Catalysts 2022) [paper]
• TrackMate 7: integrating state-of-the-art segmentation algorithms into tracking pipelines. (Nature Methods 2022) [paper]
• Deep Neural Network for Combined Particle Tracking and Colocalization Analysis in Two-Channel Microscopy Images. (IEEE 19th International Symposium on Biomedical Imaging (ISBI) 2022) [paper]

2021

• Multifluorescence Single Extracellular Vesicle Analysis by Time-Sequential Illumination and Tracking. (ACS Nano 2021) [paper]
• Deep probabilistic tracking of particles in fluorescence microscopy images. (Medical Image Analysis 2021) [paper]
• Deep Learning For Particle Detection And Tracking In Fluorescence Microscopy Images. (IEEE 18th International Symposium on Biomedical Imaging (ISBI) 2021) [paper]

2020

• Overview: Computer vision and machine learning for microstructural characterization and analysis. (Metallurgical and Materials Transactions A 2020) [paper]
• Nanoparticle recognition on scanning probe microscopy images using computer vision and deep learning. (Nanomaterials 2020) [paper]
• Towards a Computer Vision Particle Flow. (The European Physical Journal C 2020) [paper]
• Machine learning for 3D particle tracking in granular gases. (Microgravity Science and Technology 2020) [paper]
• A Recurrent Neural Network for Particle Tracking in Microscopy Images Using Future Information, Track Hypotheses, and Multiple Detections. (IEEE Trans Image Process 2020) [paper]
• Multiple Object Tracking for Occluded Particles. (IEEE Access 2020) [paper]
• Deep-learning method for data association in particle tracking. (Bioinformatics 2020) [paper]

2019

• Particle identification in camera image sensors using computer vision. (Astroparticle Physics 2019) [paper]
• PIXER: an automated particle-selection method based on segmentation using a deep neural network. (BMC bioinformatics 2019) [paper]

2017

• TrackMate: An open and extensible platform for single-particle tracking. (Methods 2017) [paper]
• Single particle tracking: from theory to biophysical applications. (Chemical reviews 2017) [paper]

2016

• Automated single particle detection and tracking for large microscopy datasets. (The Royal Society 2016) [paper]

2015

• A review of progress in single particle tracking: from methods to biophysical insights. (Reports on progress in physics 2015) [paper]

2014

• Nanoparticle Characterization Using Nanoparticle Tracking Analysis. (Nanoparticles' Promises and Risks 2014) [paper]
• Objective comparison of particle tracking methods. (Nature methods 2014) [paper]

2011

• Tracking multiple particles in fluorescence microscopy images via probabilistic data association. (IEEE International Symposium on Biomedical Imaging: From Nano to Macro 2011) [paper]

2008

• Robust single-particle tracking in live-cell time-lapse sequences. (Nature Methods 2008) [paper]

Citation

If you find our work and this repository useful, please consider giving a star ⭐ and citation 🍺:

@misc{chen2024future,
      title={Future Manufacturing with AI-Driven Particle Vision Analysis in the Microscopic World}, 
      author={Guangyao Chen and Fengqi You},
      year={2024},
      archivePrefix={arXiv},
      primaryClass={cs.AI}
}

Contributing

Please help us improve the above listing by submitting PRs of other papers in this space. Thank you!