Samsi Siddharth, Jones Michael, Kepner Jeremy, Reuther Albert
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:640-643. doi: 10.1109/EMBC.2018.8512419.
Histopathology is a critical tool in the diagnosis and stratification of cancer. Digital Pathology involves the scanning of stained and fixed tissue samples to produce highresolution images that can be used for computer-aided diagnosis and research. A common challenge in digital pathology related to the quality and characteristics of staining, which can vary widely from center to center and also within the same institution depending on the age of the stain and other human factors. In this paper we examine the use of deep learning models for colorizing H&E stained tissue images and compare the results with traditional image processing/statistical approaches that have been developed for standardizing or normalizing histopathology images. We adapt existing deep learning models that have been developed for colorizing natural images and compare the results with models developed specifically for digital pathology. Our results show that deep learning approaches can standardize the colorization of H&E images. The performance as measured by the chi-square statistic shows that the deep learning approach can be nearly as good as current state-of-the art normalization methods.
组织病理学是癌症诊断和分层的关键工具。数字病理学涉及对染色和固定的组织样本进行扫描，以生成可用于计算机辅助诊断和研究的高分辨率图像。数字病理学中的一个常见挑战与染色的质量和特征有关，不同中心之间以及同一机构内，染色质量和特征可能因染色时间和其他人为因素而有很大差异。在本文中，我们研究了使用深度学习模型对苏木精-伊红（H&E）染色的组织图像进行着色，并将结果与为使组织病理学图像标准化或归一化而开发的传统图像处理/统计方法进行比较。我们采用了为自然图像着色而开发的现有深度学习模型，并将结果与专门为数字病理学开发的模型进行比较。我们的结果表明，深度学习方法可以使H&E图像的着色标准化。通过卡方统计量衡量的性能表明，深度学习方法几乎可以与当前最先进的归一化方法相媲美。
