National Engineering Research Center of Ophthalmology and Optometry, School of Biomedical Engineering, Eye Hospital, Wenzhou Medical University, Xueyuan Road 270, Wenzhou, 325027, China.
Department of Neurosurgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China.
Comput Biol Med. 2024 Sep;180:108958. doi: 10.1016/j.compbiomed.2024.108958. Epub 2024 Aug 1.
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 mm/s. This innovative approach will pave the way for a novel, expedited route in histological staining.
苏木精-伊红（H&E）染色是诊断神经胶质瘤的关键技术，可以直接观察组织结构。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的病理学家，因此成本高、劳动强度大且耗时。有鉴于此，我们结合深度学习方法和高光谱成像技术，旨在准确快速地将高光谱图像转换为虚拟 H&E 染色图像。该方法通过在不同波长下捕获组织信息来克服 H&E 染色的局限性，提供全面详细的组织成分信息，作为真实的 H&E 染色。与各种生成器结构相比，Unet 具有显著的整体优势，其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理时间。我们开发了一种用于虚拟 H&E 染色的综合软件系统，该系统集成了 CCD 控制、显微镜控制和虚拟 H&E 染色技术，以促进快速术中成像、促进疾病诊断，并加速医疗自动化的发展。该平台以 3.81mm/s 的高速重建大尺寸神经胶质瘤的虚拟 H&E 染色图像。这种创新方法将为组织学染色开辟一条新颖、快速的途径。
