The Core Problem
Manual segmentation of retinal layers is time-consuming and subject to inter-observer variability, which limits the scale and consistency of longitudinal medical research.
System Architecture & Logic
This project leverage SAM2 and transformer-based vision architectures to automate retinal layer segmentation for clinical research. It establishes a high-fidelity pipeline for longitudinal tracking of retinal changes, delivering deterministic measurements of disease progression across large data sets.
Implementation Strategy
- Imported automatically via GitHub connection.