Liquid Biopsy Image Segmentation

This repository contains tools and models for semantic and instance segmentation of liquid biopsy microscope images.

Features

• Traditional image processing based segmentation
• Deep learning based semantic and instance segmentation
• Utilities for data preprocessing and augmentation
• Evaluation metrics and visualization tools

Repository Structure

.
├── data/                    # Dataset storage
│   ├── raw/                 # Original microscope images
│   ├── processed/           # Preprocessed images
│   └── annotations/         # Ground truth masks and annotations
├── src/                     # Source code
│   ├── traditional/         # Traditional image processing algorithms
│   ├── deep_learning/       # Deep learning models and training
│   ├── utils/               # Utility functions
│   ├── preprocessing/       # Data preprocessing scripts
│   └── evaluation/          # Model evaluation code
├── models/                  # Trained models
│   ├── traditional/         # Parameters for traditional methods
│   └── deep_learning/       # Saved weights for deep learning models
├── notebooks/               # Jupyter notebooks for experimentation
├── configs/                 # Configuration files
├── results/                 # Experimental results
│   ├── visualizations/      # Visualizations of segmentation results
│   └── metrics/             # Quantitative evaluation data
├── tests/                   # Unit tests
└── docs/                    # Documentation

Getting Started

1. Clone this repository
2. Install dependencies: pip install -r requirements.txt
3. Follow the instructions in the documentation to run segmentation models

License

[Your license information here]