多源OCT图像中精准分割视网膜积液并标记不可靠区域，助力临床决策。
Uncertainty-Aware Multi-Source Retinal Fluid Segmentation in OCT
- 融合领域自适应归一化与不确定性估计的注意力引导TransUNet
- 跨四类设备平均液体Dice达0.78，专家分歧处不确定性高1.34倍
- 输出带可信度的分割图，适合临床辅助诊断使用
通过光学相干断层扫描（OCT）测量视网膜积液对黄斑疾病治疗决策至关重要，但人工标注耗时且单一设备训练的分割模型在其他设备上性能下降。本文提出一种注意力引导的TransUNet，可在四个独立OCT来源上分割三种积液类型，结合领域自适应归一化与不确定性估计，可识别不可靠像素。模型平均液体Dice达到0.78，最关键的是，在专家标注存在分歧的位置，其不确定性高出1.34倍（p<10^-4），将原始分割图转化为可操作的临床分诊信号。
原文摘要 · Abstract (English)
Measuring retinal fluid from optical coherence tomography (OCT) drives treatment decisions in macular disease, but manual annotation is slow and segmentation models trained on one scanner degrade on another. We present an attention-guided TransUNet that segments three fluid types across four independent OCT sources, combining a domain-adaptive normalisation scheme with an uncertainty estimate that flags unreliable pixels. The model reaches a mean fluid Dice of 0.78, and -- most usefully for clinicians -- its uncertainty is 1.34x higher exactly where expert graders disagree (p<10^-4), turning a raw segmentation map into an actionable clinical triage signal.
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