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Miguel Madrid-Mencía

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

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
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  • 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
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Manual MeasurementAutomatic Algorithm Measurement
Manual Measurement
Automatic Measurement
Manual: 334 μmAlgorithm: 304 μm

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