📑 Table of Contents

1. 🔎 ParticleAnalyzer
2. ✅ Examples
3. ✨ Key Features
4. 📥 Installation Guide
5. 🛠 Segmentation Optimization Guide
6. 📊 Analysis Outputs
7. ⚙️ Advanced Settings
8. 📏 Scale Calibration
9. 📧 Contributors
10. 📖 Citation

ParticleAnalyzer

A Computer Vision-Based Tool for Automatic Segmentation and Size Analysis of Nanoparticles in Scanning Electron Microscope (SEM) and Transmission Electron Microscope (TEM) Images.

Video demonstrations:

Local video (MP4) | YouTube demonstration

If you encounter any errors while using Particle Analyzer, please open an issue in the GitHub repository or contact me at rybakov-ks@ya.ru for support. If the model cannot segment your images correctly, please send them to rybakov-ks@ya.ru . Your images will be used to retrain the model's.

✅ Examples

Example.1.mp4	Example.2.mp4
Example.3.mp4	Example.4.mp4
Example.5.mp4

✨ Key Features

• Automated particle segmentation in SEM images
• SAHI mode enables accurate detection of small particles in high-resolution images via a sliding window method
• Comprehensive statistical analysis of particle characteristics
• Interactive visualization of size distributions
• Dual unit support — switch between pixels and micrometers (µm)
• Supports multiple AI models: YOLOv11, YOLOv12, RF-DETR Seg (Preview) and Detectron2
• Advanced configuration options for fine-tuning detection accuracy
• AI Interpretation of SEM Data
• Multi-language interface: Russian, Simplified Chinese, Traditional Chinese, English (ru, zh-CN, zh-TW, en)
• Try it online: particleanalyzer.ru

🛠 Installation Guide

1. 📥 Install PyTorch with CUDA support

Make sure your system has an NVIDIA GPU with CUDA. Install PyTorch using the appropriate CUDA version (e.g., CUDA 11.8):

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

If you do not have a CUDA-capable GPU, use the CPU version instead—however, in this case, ParticleAnalyzer will run significantly slower:

pip install torch torchvision torchaudio

🧪 2. Install Detectron2 (Optional)

If you want to enable advanced instance segmentation, install Detectron2:

pip install 'git+https://github.com/facebookresearch/detectron2.git'

Warning

There may be problems installing Detectron2. Use the official documentation.

📦 3. Install ParticleAnalyzer

Finally, install ParticleAnalyzer from PyPI:

pip install --upgrade ParticleAnalyzer

✅ Now you're ready to run the application:

ParticleAnalyzer run

Open in browser: http://127.0.0.1:8000

You can specify the port if necessary:

ParticleAnalyzer run --port 5000

Launch with LLM support (OpenRouter or Hugging Face API key required):

ParticleAnalyzer run --port 5000 --api-key YOUR_OPENROUTER_API_KEY

🛠 Segmentation Optimization Guide

🔧 Core Parameters:

• Model Selection
• Detection Confidence Threshold (0-1)
  • Increase (e.g., 0.7→0.85) to reduce false positives
  • Decrease (e.g., 0.5→0.3) to detect faint particles
• IoU Threshold (0-1)
  • Increase (e.g., 0.5→0.7) to eliminate duplicate detections
  • Decrease for dense particle fields
• Enable SAHI Processing (split-analyze-merge)

🧩 SAHI Configuration (for large images):

• Slice Size: Start with 400×400
• Overlap Ratio: 0.2-0.3 (prevents edge artifacts)
  SAHI mode helps detect small objects in high-resolution images by using a sliding window approach

🔄 Model Selection:

Model	Best For	Speed	Recommended Use Case
YOLOv11	General use (balanced)	⚡⚡⚡ Fast	Quick analysis of standard samples
YOLOv12	High precision detection	⚡⚡⚡ Fast	Critical measurements
RF-DETR Seg (Preview) 🆕	Transformer-based segmentation	⚡⚡⚡ Fast	Precise object counting and segmentation with limited density (200 objects per image)
Mask R-CNN X152	Challenging morphology	⚡ Slow	Irregular/overlapping particles

📊 Analysis Outputs

Statistical Data Table

Comprehensive metrics including mean, median, min/max, standard deviation values for:

• Area (px² or µm²)
• Perimeter (px or µm)
• Equivalent diameter (px or µm)
• Feret diameters and angles (px or µm and °)
• Eccentricity (unitless)
• Intensity values (grayscale units)

Size Distribution Visualization

Normal distribution fitting for all measured parameters showing particle population characteristics

Data filtering

Filtering.data.mp4

AI Interpretation of SEM Data

Advanced Settings Panel

Configuration options include:

• Model Selection: YOLOv11, YOLOv12, Detectron2
• SAHI Mode: Enable/disable sliced inference for large images

• Detection Threshold: Confidence level (0-1)
• IOU Threshold: Overlap threshold for NMS (0-1)
• Max Detections: Maximum number of particles to detect
• Scaling Mode: Pixel/µm unit selection
• Image Resolution: Output resolution control
• Result Rounding: Decimal places for metrics
• Single Particle Mode: Detailed individual analysis
• Histogram Bins: Number of intervals for distribution plots

📐 Scale Calibration

instruction.mp4

Micrometer values are calculated by:

1. Identifying the SEM image's scale bar using two marker points
2. Manually specifying the known real-world distance between markers
3. Automatically computing the pixel-to-µm conversion ratio

Note: For accurate µm measurements, please ensure:

• The scale bar is clearly visible in your image
• Enter the correct scale
• The scale bar was created at the same magnification as your particles

📧 Contributors

Kirill Rybakov, PhD | Chemistry
Affiliation: Saratov State University
Email: rybakov-ks@ya.ru

📖 Citation

If you plan to publish results obtained using ParticleAnalyzer, we would appreciate it if you mention the use of ParticleAnalyzer (https://github.com/rybakov-ks/ParticleAnalyzer).