用轨迹对齐流模型，让低质OCT图像在测试时自适应变清晰。
Test-Time Adaptation in Optical Coherence Tomography Using Trajectory-Aligned Time-Independent Flow
- 基于流匹配生成高质图像，对齐输入与预期分布
- 在两种老年黄斑变性阶段分割任务中达最优性能
- 适合医疗影像质量不稳场景的实时优化
光学相干断层扫描（OCT）在眼科诊断中至关重要，但低成本设备图像质量不稳定，影响自动化分析。针对这一问题，我们提出一种基于流匹配的测试时自适应方法，从噪声输入生成高质量替代图像。通常，测试与训练数据间的域差异会导致去噪过程中的像素分布不匹配。我们通过将测试图像直方图与合成参考轨迹对齐，成功使输入分布与期望分布一致。同时，移除网络的时间条件以应对真实噪声分布的微小偏差。该方法在两种年龄相关性黄斑变性（AMD）阶段的关键生物标志物分割任务中达到当前最佳表现。代码已开源：https://github.com/Veit21/tta-flow。
原文摘要 · Abstract (English)
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.
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