Computer Vision adapted to Optical Coherence Tomographies (Bachelor’s Thesis)

This project was developed as a Bachelor’s Thesis at the Universidad Complutense de Madrid in collaboration with the Ophthalmology service of Hospital 12 de Octubre.

Key Contributions:

• Automated Diagnostics: Designed and implemented a computer vision algorithm to automate the measurement of uvea thickness from Optical Coherence Tomography (OCT) images.
• Clinical Monitoring: Developed tools to monitor the progression of uveitis, providing quantitative data to support clinical decision-making.
• Technology Stack: Leveraged Python with OpenCV bindings to perform image segmentation, feature extraction, and comparative analysis between manual and automatic measurements.
• Impact: Replaced or supplemented time-consuming manual measurements with a consistent, reproducible algorithmic approach.

Results & Visualization:

Manual Measurement	Automatic Algorithm Measurement
Example of manual annotation	Algorithm output showing automated thickness calculation

Skills: Computer Vision, Python, OpenCV, Medical Imaging.