📑 Table of Contents

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

ParticleAnalyzer

<div align="left"> <a href="https://particleanalyzer.ru/"> <img src="./Images/Logo.png" alt="ParticleAnalyzer Logo" width="300"/> </a> </div> <p>A Computer Vision-Based Tool for Automatic Segmentation and Size Analysis of Nanoparticles in Scanning Electron Microscope (SEM) and Transmission Electron Microscope (TEM) Images.</p> <div align="center"> <h3>Video demonstrations:</h3> <p> <a href="Images/ParticleAnalyzer.mp4">Local video (MP4)</a> | <a href="https://youtu.be/qlCuZDjDyqk">YouTube demonstration</a> </p> </div> <div align="center"> <img src="Images/example.gif" alt="Example"> </div>

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 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 | | YOLOv26 | Ultra precision | ⚡⚡⚡⚡ Fast | Measurements of complex samples | | 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 |

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📊 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).

ParticleAnalyzer

Install / Use

README

📑 Table of Contents