提出染色感知的显微镜聚焦质量评估方法，解决不同染色导致的成像差异问题。
FluoCLIP: Stain-Aware Focus Quality Assessment in Fluorescence Microscopy
- 基于视觉语言模型，结合染色语义实现染色条件下的聚焦排序
- 在多组织、多染色、多聚焦水平数据集上显著优于传统方法
- 适合需要高精度聚焦评估的生物医学图像分析研究者
荧光显微镜中的准确聚焦质量评估（FQA）因染色相关的光学变化而困难，这些变化导致图像间聚焦行为异质。现有方法将聚焦质量视为与染色无关的问题，假设存在统一的全局排序。本文提出染色感知的FQA，表明由于染色依赖的成像特性，聚焦排名关系在不同染色间存在显著差异，从而推翻了该假设。为支持此观点，我们构建了首个涵盖多种组织、荧光染色及聚焦水平的染色感知FQA数据集FluoMix。进一步提出FluoCLIP，一种两阶段视觉-语言框架，通过融入染色语义实现染色条件下的序数推理，有效解耦染色表示与序数结构。通过显式建模染色依赖的聚焦行为，FluoCLIP在多样化的荧光显微镜条件下持续超越传统FQA方法与近期视觉-语言基线，展现出强泛化能力。代码与数据集公开获取：https://fluoclip.github.io/。
原文摘要 · Abstract (English)
Accurate focus quality assessment (FQA) in fluorescence microscopy is challenging due to stain-dependent optical variations that induce heterogeneous focus behavior across images. Existing methods, however, treat focus quality as a stain-agnostic problem, assuming a shared global ordering. We formulate stain-aware FQA for fluorescence microscopy, showing that focus-rank relationships vary substantially across stains due to stain-dependent imaging characteristics and invalidate this assumption. To support this formulation, we introduce FluoMix, the first dataset for stain-aware FQA spanning multiple tissues, fluorescent stains, and focus levels. We further propose FluoCLIP, a two-stage vision-language framework that grounds stain semantics and enables stain-conditioned ordinal reasoning for focus prediction, effectively decoupling stain representation from ordinal structure. By explicitly modeling stain-dependent focus behavior, FluoCLIP consistently outperforms both conventional FQA methods and recent vision-language baselines, demonstrating strong generalization across diverse fluorescence microscopy conditions. Code and dataset are publicly available at https://fluoclip.github.io/.
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