Flow-matching TTA with histogram matching to synthetic reference trajectories and time-independent flow achieves SOTA segmentation of AMD biomarkers in OCT.
Self-Supervised
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Presents a fully automated deep learning framework for pixel-wise segmentation of RPE loss, EZ loss, and EZ thinning in SD-OCT volumes for GA monitoring, validated on external data with high accuracy metrics.
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Test-Time Adaptation in Optical Coherence Tomography Using Trajectory-Aligned Time-Independent Flow
Flow-matching TTA with histogram matching to synthetic reference trajectories and time-independent flow achieves SOTA segmentation of AMD biomarkers in OCT.
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Fully Automated High-Precision Segmentation of Retinal Atrophy and Ellipsoid Zone Thickness in OCT: A Reliable Tool for Real-World GA Monitoring
Presents a fully automated deep learning framework for pixel-wise segmentation of RPE loss, EZ loss, and EZ thinning in SD-OCT volumes for GA monitoring, validated on external data with high accuracy metrics.