Test-Time Adaptation in Optical Coherence Tomography Using Trajectory-Aligned Time-Independent Flow
It addresses the problem of inconsistent OCT image quality in low-cost devices, which hinders automated analysis in ophthalmology.
The paper introduces a flow-matching-based test-time adaptation method for OCT that generates high-quality surrogate images from noisy inputs, achieving state-of-the-art segmentation performance for two stages of AMD.
Optical coherence tomography (OCT) is essential in ophthalmology, but inconsistent image quality especially in low-cost devices hinders automated analysis. To address this, we introduce a flow-matching-based test-time adaptation method that generates high-quality surrogate images from noisy inputs. Typically, domain gaps between test and training data cause pixel distribution mismatches during the denoising process. We overcome this by matching the test image's histogram to synthetic reference trajectories, successfully aligning the input with expected distributions. Additionally, we remove the network's time conditioning to account for slight deviations in real-world noise distributions. Our approach achieves state-of-the-art performance in segmenting critical biomarkers for two stages of Age-related Macular Degeneration (AMD). Code is available: https://github.com/Veit21/tta-flow.