Advancing Opthalmic Diagnostics: U-Net for Retinal Blood Vessel Segmentation

Abstract

This research project focuses on the development and evaluation of an advanced algorithm for retinal vessel segmentation, a critical component in the automated analysis of retinal images for diagnosing ocular diseases. Leveraging state-of-the-art image processing techniques and deep learning models, we propose a novel segmentation algorithm that significantly enhances the accuracy and efficiency of identifying retinal blood vessels from fundus photographs. Our methodology encompasses a comprehensive data preparation phase, including image normalization and augmentation, to improve the model's robustness and generalizability. We implemented a convolutional neural network (CNN)-based architecture optimized for the intricate patterns and variations inherent in retinal images. The performance of our algorithm was rigorously evaluated against established benchmarks, demonstrating superior precision, recall, and a higher Dice coefficient compared to existing methods. These findings indicate the potential of our approach to contribute substantially to the early detection and monitoring of ocular conditions such as diabetic retinopathy and glaucoma. The research underscores the importance of advanced computational techniques in enhancing the diagnostic capabilities of retinal image analysis and sets the stage for future innovations in medical imaging.