苏木素和伊红(H&E)染色是诊断神经胶质瘤的关键技术,允许直接观察组织结构。然而,H&E染色工作流程需要复杂的处理,专门的实验室基础设施,和专业病理学家,渲染它昂贵,劳动密集型,而且耗时。鉴于这些考虑,我们将深度学习方法和高光谱成像技术相结合,旨在将高光谱图像准确快速地转换为虚拟H&E染色图像。该方法通过捕获不同波长的组织信息,克服了H&E染色的局限性,提供全面和详细的组织组成信息作为现实的H&E染色。与各种发电机结构相比,Unet表现出巨大的整体优势,平均结构相似性指数度量(SSIM)为0.7731,峰值信噪比(PSNR)为23.3120,以及最短的训练和推断时间。一个全面的虚拟H&E染色软件系统,集成了CCD控制,显微镜控制,和虚拟H&E染色技术,是为了促进快速术中成像,促进疾病诊断,加快医疗自动化的发展。该平台以3.81mm2/s的高速度重建神经胶质瘤的大规模虚拟H&E染色图像。这种创新的方法将为小说铺平道路,组织学染色的加快路线。
Hematoxylin and eosin (H&E) staining is a crucial technique for diagnosing glioma, allowing direct observation of tissue structures. However, the H&E staining workflow necessitates intricate processing, specialized laboratory infrastructures, and specialist pathologists, rendering it expensive, labor-intensive, and time-consuming. In view of these considerations, we combine the deep learning method and hyperspectral imaging technique, aiming at accurately and rapidly converting the hyperspectral images into virtual H&E staining images. The method overcomes the limitations of H&E staining by capturing tissue information at different wavelengths, providing comprehensive and detailed tissue composition information as the realistic H&E staining. In comparison with various generator structures, the Unet exhibits substantial overall advantages, as evidenced by a mean structure similarity index measure (SSIM) of 0.7731 and a peak signal-to-noise ratio (PSNR) of 23.3120, as well as the shortest training and inference time. A comprehensive software system for virtual H&E staining, which integrates CCD control, microscope control, and virtual H&E staining technology, is developed to facilitate fast intraoperative imaging, promote disease diagnosis, and accelerate the development of medical automation. The platform reconstructs large-scale virtual H&E staining images of gliomas at a high speed of 3.81 mm2/s. This innovative approach will pave the way for a novel, expedited route in histological staining.