OCT Retinal-Layer Segmentation
A U-Net pipeline for pixel-wise retinal-layer segmentation from OCT B-scans, built as an optometrist moving into computational vision
🔬 Research
Research Interests
01
Computational Ophthalmic Imaging
- OCT analysis
- Retinal / posterior eye structure
- Segmentation and quantitative characterization
- 3D ocular modelling
02
AI for Vision Science
- Computer vision
- Deep learning
- AI-assisted vision assessment
- Clinical decision support
03
Human-Centred Vision Technology
- Assistive vision systems
- Accessibility
- Real-world evaluation
- On-device inference
Research Trajectory
My research interests have evolved from clinical optometry toward computational approaches to understanding and improving human vision. My earlier work explored AI-assisted mobility and vision assessment in real-world settings. I am now extending this foundation into ophthalmic imaging, focusing on OCT-based retinal analysis, image segmentation, and quantitative modelling of ocular structure.
Projects
Publications
Awards
Caraham Ophthalmics Prize for Innovation in Optometry
For the student who has demonstrated creativity and innovation in clinical practice, technology or research, with emphasis on improving the lives of people with vision loss.
Research Skills
🧠 PyTorch 🩻 Medical Image Segmentation 🔷 U-Net / CNN Architectures 📊 Dice / IoU Evaluation 🗂️ Dataset Curation & Splitting 🎥 Real-Time Computer Vision 📱 On-Device Inference 🧬 Deep Learning Classification 🔁 Data Augmentation 🧪 Mixed-Method Research 🙋🏾 Usability & Task-Completion Testing 👁️ Vision Science Domain Knowledge