MedSAM2 for Medical Image Segmentation

Overview

Implemented and evaluated structure-aware medical image segmentation workflows for retinal OCT analysis using MedSAM2 — a medical adaptation of the Segment Anything Model 2 architecture. The project focused on improving segmentation quality under challenging clinical imaging conditions.

Problem

Retinal OCT (Optical Coherence Tomography) scans present unique segmentation challenges: thin layered structures, noise, and artifacts that confuse standard vision models. Translating foundation-style vision models like SAM2 into medical settings requires careful adaptation and evaluation.

Why It Mattered

Medical image segmentation directly supports clinical diagnosis and treatment planning. Improving the accuracy and reliability of automated segmentation in retinal imaging can accelerate research, reduce manual annotation burden, and support earlier detection of retinal diseases.

Approach

1. Architecture adaptation: Applied MedSAM2 to retinal OCT data, exploring structure-aware modifications for thin-layer segmentation.
2. Evaluation pipeline: Built comprehensive evaluation workflows to measure segmentation quality across multiple metrics and edge cases.
3. Clinical grounding: Worked within the constraints of limited labeled medical data and clinical imaging variability.

Results & Impact

• Improved segmentation performance on retinal OCT data compared to baseline approaches
• Strengthened practical experience translating foundation-style vision models into medical AI settings
• Developed reusable evaluation pipelines for medical segmentation tasks

Lessons Learned

• Foundation models are powerful starting points, but medical domains require domain-specific adaptation and rigorous evaluation
• Limited labeled data is the norm in medical AI — data efficiency matters more than model complexity
• Close collaboration with domain experts is essential for meaningful evaluation