Obtaining particle coordinates from microscopy images using machine learning (ML) techniques has become an important area of research in the study of colloidal systems, as well as in other fields such as biology and materials science. Traditionally, particle tracking and coordinate extraction were carried out using manual or semi-automated methods. However, with advances in machine learning, especially deep learning, particle localization and tracking can now be achieved with greater accuracy, speed, and robustness, even in challenging environments (e.g., crowded suspensions, noisy images, or low-contrast images).

Key Steps in Obtaining Particle Coordinates via Machine Learning

Here’s an outline of the general approach to using machine learning to extract particle coordinates from microscopy images:

1. Preprocessing of Microscopy Images

Before applying machine learning models, it is important to prepare the microscopy images for accurate particle detection. Common preprocessing steps include:

• Noise Reduction: Microscopy images often contain noise due to factors like sensor limitations, environmental conditions, or light scattering. Gaussian filtering or median filtering can be used to smooth the images and reduce noise.
• Contrast Enhancement: Sometimes, the particles are faint or not well contrasted with the background. Histogram equalization or adaptive histogram equalization can improve the contrast in these cases.
• Thresholding: Simple thresholding or advanced methods (like Otsu’s thresholding) can be used to segment particles from the background by converting the image into a binary form (particles = white, background = black).

2. Particle Detection Using Machine Learning

Once the image has been preprocessed, the task is to detect and locate particles (i.e., extract their coordinates) in the image. The main challenge is to separate the particles from the background and localize them accurately.

a. Traditional Methods (Non-ML-Based)

Before diving into machine learning-based techniques, traditional methods like blob detection (e.g., Laplacian of Gaussian (LoG), Difference of Gaussian (DoG), or Hough Transform) can be used to identify potential particle locations. However, these methods often fail in noisy or overlapping particle systems.

b. Machine Learning Models for Particle Detection

• Supervised Learning Models:

  • Supervised models require a labeled dataset of microscopy images with ground truth particle locations (coordinates). A typical training dataset consists of images where the particle positions are annotated manually or with a high-precision method.

  Common approaches include:

  • Convolutional Neural Networks (CNNs):
    • CNNs are widely used for image analysis tasks because they can automatically learn features from raw images. They can be trained to identify and localize particles by feeding them microscopy images and their corresponding coordinates (in a regression format or as class labels in object detection tasks).
    • U-Net: A type of CNN designed for semantic segmentation tasks that has been widely used in particle localization. The network outputs a probability map showing where particles are likely to be located.
    • Region-based CNN (R-CNN) or YOLO (You Only Look Once): These are object detection models that can be adapted for particle detection. They output bounding boxes around particles and can be used to extract particle coordinates.
  • Deep Learning-Based Approaches:
    • In deep learning, models can directly predict the coordinates of the particles from microscopy images.
    • Fully Convolutional Networks (FCNs) or DeepLab can be used for pixel-wise segmentation, which helps locate particles by mapping image pixels to particle centers. This approach is particularly useful when dealing with dense or overlapping particle systems.
  • Regression Networks:
    • Some ML models directly output the coordinates of individual particles by learning to map input image features to coordinates (x, y). These models are trained on annotated datasets where the ground truth particle positions are provided.

c. Unsupervised Learning Models

In some cases, obtaining ground truth data for supervised learning may not be feasible. Unsupervised learning or semi-supervised learning can be used for particle localization in these situations.

• Clustering Algorithms:
  • Techniques like k-means clustering, DBSCAN (Density-Based Spatial Clustering of Applications with Noise), or mean-shift clustering can identify clusters of pixels corresponding to particle centers in an image. These algorithms can be used to detect particle locations without requiring labeled data, although they may need careful tuning for accuracy.
• Autoencoders:
  • Autoencoders can be trained to learn representations of particle distributions in images. These can then be used to identify regions where particles are likely to be located.