Single Cell Segmentation for huvec cell(lung cancer) using U-Net Architectures

Overview

This repository contains the implementation of advanced approach to single cell segmentation for lung cancer cells using a series of U-Net architectures: U-Net, Nested U-Net (UNet++), and U^2-Net. These models are renowned for their efficiency and accuracy in biomedical image segmentation tasks. This project aims to leverage these architectures to improve the precision of lung cancer cell segmentation specifically for 16bit FRET image segmentation.

Features

• Implementation of U-Net, Nested U-Net (UNet++), and U^2-Net for precise single cell segmentation.
• Application to lung cancer cell data for detailed cell morphology analysis.
• Preprocessing scripts for image data preparation.
• Evaluation scripts for model performance assessment.

Installation

To set up the environment for this project, follow these steps:

git clone https://github.com/Mengxicici/HuvecCellSeg.git
cd HuvecCellSeg
pip install -r requirements.txt