FluoCLIP: Stain-Aware Focus Quality Assessment in Fluorescence Microscopy

TL;DR

This work forms the task of stain-aware FQA, emphasizing that focus behavior in fluorescence microscopy must be modeled as a function of staining characteristics, and proposes FluoCLIP, a two-stage vision-language framework that achieves strong generalization across diverse fluorescence microscopy conditions.

Abstract

Accurate focus quality assessment (FQA) in fluorescence microscopy remains challenging, as the stain-dependent optical properties of fluorescent dyes cause abrupt and heterogeneous focus shifts. However, existing datasets and models overlook this variability, treating focus quality as a stain-agnostic problem. In this work, we formulate the task of stain-aware FQA, emphasizing that focus behavior in fluorescence microscopy must be modeled as a function of staining characteristics. Through quantitative analysis of existing datasets (FocusPath, BBBC006) and our newly curated FluoMix, we demonstrate that focus-rank relationships vary substantially across stains, underscoring the need for stain-aware modeling in fluorescence microscopy. To support this new formulation, we propose FluoMix, the first dataset for stain-aware FQA that encompasses multiple tissues, fluorescent stains, and focus variations. Building on this dataset, we propose FluoCLIP, a two-stage vision-language framework that leverages CLIP's alignment capability to interpret focus quality in the context of biological staining. In the stain-grounding phase, FluoCLIP learns general stain representations by aligning textual stain tokens with visual features, while in the stain-guided ranking phase, it optimizes stain-specific rank prompts for ordinal focus prediction. Together, our formulation, dataset, and framework establish the first foundation for stain-aware FQA, and FluoCLIP achieves strong generalization across diverse fluorescence microscopy conditions.

Figures (5)

• Figure 1: Modality gap in focus quality assessment. Simple edge-based models such as FocusLiteNN wang2020focuslitenn perform reliably on bright-field datasets like FocusPath hosseini2019encoding, where blur is spatially uniform, but struggles on fluorescence datasets (BBBC006 ljosa2012annotated and FluoMix) that exhibit stain-dependent, non-uniform defocus— underscoring the need for stain-aware FQA.
• Figure 2: Overview of FluoCLIP Framework. FluoCLIP consists of two sequential stages for stain-aware focus quality assessment (FQA). In the Stage 1: Stain-Grounding, learnable stain tokens are aligned with CLIP’s visual encoder through an adapter to form stain-specific textual features. In the Stage 2: Stain-Guided Ranking, these grounded stain embeddings are used to condition rank prompts via interpolation and projection, producing stain-aware focus rank representations. Both stages jointly enable FluoCLIP to model stain-dependent focus behavior and align text–image features for stain-aware FQA.
• Figure 3: Empirical Analysis of Stain-Dependent Focus Behavior: (a) Mean spatial frequency (SF) versus focus rank for three datasets; the shaded region indicates $\pm$1 standard deviation across samples. SF decreases monotonically with increasing rank, confirming that SF reliably captures focus degradation. (b)--(d) Boxplots of SF values across stains for each dataset (x-axis: stain identity, y-axis: SF distribution). FocusPath shows stain-invariant SF trends, wherease BBBC006 and FluoMix display pronounced stain-dependent variability.
• Figure 4: Examples of dataset classes: The figure displays samples of images from three datasets across different focus levels. (Top) FocusPath dataset images numbered from 0 to 11, each showing different staining techniques. (Middle) BBBC006 dataset images numbered from 0 to 9. (Bottom) FluoMix dataset images numbered from 0 to 9, representing different focus levels.
• Figure 5: Sample images illustrating stain diversity in the three datasets. (Top) FocusPath: bright-field microscopy images with eight different histological dyes. (Bottom left) BBBC006: fluorescence microscopy images of cell lines labeled with Hoechst 33342 and Phalloidin. (Bottom right) FluoMix: tissue-level fluorescence microscopy images covering six distinct fluorescent staining protocols.