📑 Table of Contents

• 🔎 ParticleAnalyzer
• ✅ Examples
• ✨ Key Features
• 📥 Installation Guide
• 🛠 Segmentation Optimization Guide
• 📊 Analysis Outputs
• ⚙️ Advanced Settings
• 📏 Scale Calibration
• 📧 Contributors
• 📖 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.

*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

✨ 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, YOLOv26, 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 YOUROPENROUTERAPI_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:

📊 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

https://github.com/user-attachments/assets/6548071a-3c83-4539-897a-6ebf175bec17

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

https://github.com/user-attachments/assets/cf2e272b-6d04-4347-abff-7a1eadaa3033

Micrometer values are calculated by:

• Identifying the SEM image's scale bar using two marker points
• Manually specifying the known real-world distance between markers
• 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).