Ma Ling, Sherey Jeremy, Palsgrove Doreen, Fei Baowei
Center for Imaging and Surgical Innovation, University of Texas at Dallas, Richardson, TX.
Department of Bioengineering, University of Texas at Dallas, Richardson, TX.
Proc SPIE Int Soc Opt Eng. 2023 Feb;12471. doi: 10.1117/12.2653715. Epub 2023 Apr 6.
Hyperspectral imaging (HSI) has been demonstrated in various digital pathology applications. However, the intrinsic high dimensionality of hyperspectral images makes it difficult for pathologists to visualize the information. The aim of this study is to develop a method to transform hyperspectral images of hemoxylin & eosin (H&E)-stained slides to natural-color RGB histologic images for easy visualization. Hyperspectral images were obtained at 40× magnification with an automated microscopic imaging system and downsampled by various factors to generate data equivalent to different magnifications. High-resolution digital histologic RGB images were cropped and registered to the corresponding hyperspectral images as the ground truth. A conditional generative adversarial network (cGAN) was trained to output natural color RGB images of the histological tissue samples. The generated synthetic RGBs have similar color and sharpness to real RGBs. Image classification was implemented using the real and synthetic RGBs, respectively, with a pretrained network. The classification of tumor and normal tissue using the HSI-synthesized RGBs yielded a comparable but slightly higher accuracy and AUC than the real RGBs. The proposed method can reduce the acquisition time of two imaging modalities while giving pathologists access to the high information density of HSI and the quality visualization of RGBs. This study demonstrated that HSI may provide a potentially better alternative to current RGB-based pathologic imaging and thus make HSI a viable tool for histopathological diagnosis.
高光谱成像（HSI）已在各种数字病理学应用中得到验证。然而，高光谱图像固有的高维度特性使得病理学家难以直观地理解其中的信息。本研究的目的是开发一种方法，将苏木精和伊红（H&E）染色玻片的高光谱图像转换为自然色彩的RGB组织学图像，以便于直观观察。使用自动显微成像系统在40倍放大倍数下获取高光谱图像，并通过不同因子进行下采样，以生成相当于不同放大倍数的数据。将高分辨率数字组织学RGB图像裁剪并配准到相应的高光谱图像上作为基准真值。训练一个条件生成对抗网络（cGAN）来输出组织学组织样本的自然色彩RGB图像。生成的合成RGB图像在颜色和清晰度方面与真实RGB图像相似。分别使用真实RGB图像和合成RGB图像，通过一个预训练网络进行图像分类。使用HSI合成的RGB图像对肿瘤组织和正常组织进行分类，其准确率和曲线下面积（AUC）与真实RGB图像相当，但略高。所提出的方法可以减少两种成像方式的采集时间，同时让病理学家能够获取高光谱图像的高信息密度以及RGB图像的高质量可视化效果。本研究表明，高光谱成像可能为当前基于RGB的病理成像提供一种潜在的更好替代方案，从而使高光谱成像成为组织病理学诊断的一种可行工具。
