This repository contains an implementation of the U-Net architecture for image segmentation tasks, specifically applied to the DRIVE dataset for vessel extraction from retinal images. Based on the extracted content, here's a comprehensive README for the provided notebook:
U-Net is a convolutional neural network architecture developed for biomedical image segmentation. The U-Net architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization.
This notebook demonstrates the application of the U-Net model for the segmentation of blood vessels in retinal images, which is a crucial step in diagnosing various retinal diseases.
The model is trained and tested on the DRIVE dataset (Digital Retinal Images for Vessel Extraction), which contains retinal images along with their corresponding segmentation masks.
- Python 3.x
- Google Colab (recommended)
- Kaggle API credentials (for dataset download)
The model's performance is evaluated using standard metrics such as accuracy, precision, recall, and the Dice coefficient. The results are visualized using segmentation masks overlaid on the original images.