让显微镜图像自动生成更精准的细胞形态图，重点突出有用信息。
Foreground-aware Virtual Staining for Accurate 3D Cell Morphological Profiling
- 通过分析像素分布自动识别细胞区域，只在关键部位计算误差
- 在3D数据集上显著提升形态特征还原度，同时保持像素精度
- 适合需要精确细胞轮廓的生物研究与自动化分析任务
显微成像可直接观察三维细胞形态，透射光方法成本低且无创，荧光显微镜则具备特异性和对比度。虚拟染色利用机器学习，从无标记输入预测荧光图像，融合两者优势。但现有方法通常使用全局均等损失函数，导致模型复制背景噪声和伪影，而非关注生物学有意义信号。本文提出Spotlight，一种简单而强大的虚拟染色方法，引导模型聚焦于相关细胞结构。Spotlight采用基于直方图的前景估计，对像素级损失进行掩码，并在软阈值预测上计算Dice损失，实现形状感知学习。应用于3D基准数据集后，Spotlight在保留像素级精度的同时显著改善形态表征，生成的虚拟染色图像更适用于下游任务，如分割与形态学分析。
原文摘要 · Abstract (English)
Microscopy enables direct observation of cellular morphology in 3D, with transmitted-light methods offering low-cost, minimally invasive imaging and fluorescence microscopy providing specificity and contrast. Virtual staining combines these strengths by using machine learning to predict fluorescence images from label-free inputs. However, training of existing methods typically relies on loss functions that treat all pixels equally, thus reproducing background noise and artifacts instead of focusing on biologically meaningful signals. We introduce Spotlight, a simple yet powerful virtual staining approach that guides the model to focus on relevant cellular structures. Spotlight uses histogram-based foreground estimation to mask pixel-wise loss and to calculate a Dice loss on soft-thresholded predictions for shape-aware learning. Applied to a 3D benchmark dataset, Spotlight improves morphological representation while preserving pixel-level accuracy, resulting in virtual stains better suited for downstream tasks such as segmentation and profiling.
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