发表机构
Newcastle University（新卡斯尔大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对OCT中视网膜液分割问题，提出注意力引导的TransUNet，结合域自适应归一化与不确定性估计，能跨四个独立OCT源分割三种液体类型，平均液体Dice系数达0.78，可将分割图转化为临床分类信号。
AI 中文摘要
从光学相干断层扫描（OCT）中测量视网膜液有助于黄斑疾病的治疗决策，但手动标注速度慢，在一台扫描仪上训练的分割模型在另一台扫描仪上性能会下降。我们提出了一种注意力引导的TransUNet，可跨四个独立的OCT源分割三种液体类型，结合域自适应归一化方案和标记不可靠像素的不确定性估计。该模型的平均液体Dice系数达到0.78，在专家分级员意见不一致的地方，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类信号。
英文摘要
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.
Comments9 pages, 2 figures, 5 tables. Code, model weights, and REST inference API are available on GitHub and Zenodo
