标题： 病理引导的虚拟染色度量用于评估和培训 显示英文标题

标题： Pathology-Guided Virtual Staining Metric for Evaluation and Training

摘要： 虚拟染色已成为传统组织病理学染色技术的有力替代方法，能够实现快速、无需试剂的图像转换。 然而，现有的评估方法主要依赖于全参考图像质量评估（FR-IQA）指标，如结构相似性，这些指标最初是为自然图像设计的，通常无法捕捉与病理相关的特征。 也已使用专家病理学审查，但它们本质上是主观的且耗时。 在本研究中，我们引入了PaPIS（病理感知的感知图像相似性），一种专门针对虚拟染色评估的新型FR-IQA指标。 PaPIS利用基于深度学习的特征，这些特征在细胞形态分割上进行了训练，并结合了受Retinex启发的特征分解，以更好地反映组织学感知质量。 比较实验表明，PaPIS更能准确地与与病理相关的视觉线索对齐，并区分传统和现有感知指标容易忽略的细微细胞结构。 此外，将PaPIS作为虚拟染色模型中的引导损失函数，可以提高组织学保真度。 这项工作突显了开发病理感知评估框架的重要性，以推动虚拟染色技术的发展和临床应用。 显示英文摘要

摘要： Virtual staining has emerged as a powerful alternative to traditional histopathological staining techniques, enabling rapid, reagent-free image transformations. However, existing evaluation methods predominantly rely on full-reference image quality assessment (FR-IQA) metrics such as structural similarity, which are originally designed for natural images and often fail to capture pathology-relevant features. Expert pathology reviews have also been used, but they are inherently subjective and time-consuming. In this study, we introduce PaPIS (Pathology-Aware Perceptual Image Similarity), a novel FR-IQA metric specifically tailored for virtual staining evaluation. PaPIS leverages deep learning-based features trained on cell morphology segmentation and incorporates Retinex-inspired feature decomposition to better reflect histological perceptual quality. Comparative experiments demonstrate that PaPIS more accurately aligns with pathology-relevant visual cues and distinguishes subtle cellular structures that traditional and existing perceptual metrics tend to overlook. Furthermore, integrating PaPIS as a guiding loss function in a virtual staining model leads to improved histological fidelity. This work highlights the critical need for pathology-aware evaluation frameworks to advance the development and clinical readiness of virtual staining technologies.