Virtual staining streamlines traditional staining procedures by digitally generating stained images from unstained or differently stained images. While conventional staining methods involve time-consuming chemical processes, virtual staining offers an efficient and low infrastructure alternative. Leveraging microscopy-based techniques, such as confocal microscopy, researchers can expedite tissue analysis without the need for physical sectioning. However, interpreting grayscale or pseudo-color microscopic images remains a challenge for pathologists and surgeons accustomed to traditional histologically stained images. To fill this gap, various studies explore digitally simulating staining to mimic targeted histological stains. This paper introduces a novel network, In-and-Out Net, specifically designed for virtual staining tasks. Based on Generative Adversarial Networks (GAN), our model efficiently transforms Reflectance Confocal Microscopy (RCM) images into Hematoxylin and Eosin (H&E) stained images. We enhance nuclei contrast in RCM images using aluminum chloride preprocessing for skin tissues. Training the model with virtual H\&E labels featuring two fluorescence channels eliminates the need for image registration and provides pixel-level ground truth. Our contributions include proposing an optimal training strategy, conducting a comparative analysis demonstrating state-of-the-art performance, validating the model through an ablation study, and collecting perfectly matched input and ground truth images without registration. In-and-Out Net showcases promising results, offering a valuable tool for virtual staining tasks and advancing the field of histological image analysis.

翻译：虚拟染色技术通过从未染色或不同染色图像中数字化生成染色图像，简化了传统染色流程。传统染色方法涉及耗时的化学处理过程，而虚拟染色提供了一种高效且低基础设施的替代方案。借助共聚焦显微镜等显微技术，研究人员无需物理切片即可加速组织分析。然而，对于习惯传统组织学染色图像的病理学家和外科医生而言，解读灰度或伪彩色显微图像仍具挑战。为填补这一空白，多项研究探索了数字化模拟染色以复现目标组织学染色。本文提出一种专为虚拟染色任务设计的新型网络——In-and-Out Net。基于生成对抗网络（GAN），该模型能高效地将反射式共聚焦显微镜（RCM）图像转换为苏木精-伊红（H&E）染色图像。我们采用氯化铝预处理皮肤组织以增强RCM图像的细胞核对比度。通过使用具有双荧光通道的虚拟H&E标签训练模型，无需图像配准即可提供像素级真实标注。本研究的贡献包括：提出最优训练策略，通过对比分析展示先进性能，通过消融实验验证模型有效性，以及收集无需配准的完美匹配输入图像与真实标注图像。In-and-Out Net展现出优异性能，为虚拟染色任务提供了有力工具，并推动了组织学图像分析领域的发展。