Opt Lett. 2024 Sep 15;49(18):5135-5138. doi: 10.1364/OL.537220.
Recently, virtual staining techniques have attracted more and more attention, which can help bypass the chemical staining process of traditional histopathological examination, saving time and resources. Meanwhile, as an emerging tool to characterize specific tissue structures in a label-free manner, the Mueller matrix microscopy can supplement more structural information that may not be apparent in bright-field images. In this Letter, we propose the Mueller matrix guided generative adversarial networks (MMG-GAN). By integrating polarization information provided by the Mueller matrix microscopy, the MMG-GAN enables the effective transformation of input H&E-stained images into corresponding Masson trichrome (MT)-stained images. The experimental results demonstrate the accuracy of the generated images by MMG-GAN and reveal the potential for more stain transformation tasks by incorporating the Mueller matrix polarization information, laying the foundation for future polarimetry-assisted digital pathology.
最近，虚拟染色技术越来越受到关注，它可以帮助绕过传统组织病理学检查的化学染色过程，节省时间和资源。同时，作为一种新兴的无标记方式来描述特定组织结构的工具，Mueller 矩阵显微镜可以补充在明场图像中可能不明显的更多结构信息。在这篇快报中，我们提出了 Mueller 矩阵引导生成对抗网络（MMG-GAN）。通过整合 Mueller 矩阵显微镜提供的偏振信息，MMG-GAN 能够有效地将输入的 H&E 染色图像转换为相应的 Masson 三色（MT）染色图像。实验结果表明了 MMG-GAN 生成图像的准确性，并揭示了通过结合 Mueller 矩阵偏振信息进行更多染色转换任务的潜力，为未来的偏振辅助数字病理学奠定了基础。
