使用全卷积神经网络预测 H&E 染色病理图像中的微血管。
Microvessel prediction in H&E Stained Pathology Images using fully convolutional neural networks.
机构信息
Quantitative Biomedical Research Center, Department of Clinical Sciences, University of Texas Southwestern Medical Center, 5325 Harry Hines Blvd, Dallas, TX, 75390, USA.
Department of Pathology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Science and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100023, P. R. China.
出版信息
BMC Bioinformatics. 2018 Feb 27;19(1):64. doi: 10.1186/s12859-018-2055-z.
BACKGROUND
Pathological angiogenesis has been identified in many malignancies as a potential prognostic factor and target for therapy. In most cases, angiogenic analysis is based on the measurement of microvessel density (MVD) detected by immunostaining of CD31 or CD34. However, most retrievable public data is generally composed of Hematoxylin and Eosin (H&E)-stained pathology images, for which is difficult to get the corresponding immunohistochemistry images. The role of microvessels in H&E stained images has not been widely studied due to their complexity and heterogeneity. Furthermore, identifying microvessels manually for study is a labor-intensive task for pathologists, with high inter- and intra-observer variation. Therefore, it is important to develop automated microvessel-detection algorithms in H&E stained pathology images for clinical association analysis.
RESULTS
In this paper, we propose a microvessel prediction method using fully convolutional neural networks. The feasibility of our proposed algorithm is demonstrated through experimental results on H&E stained images. Furthermore, the identified microvessel features were significantly associated with the patient clinical outcomes.
CONCLUSIONS
This is the first study to develop an algorithm for automated microvessel detection in H&E stained pathology images.
背景
病理性血管生成已在许多恶性肿瘤中被确定为一种潜在的预后因素和治疗靶点。在大多数情况下，血管生成分析基于免疫组织化学染色 CD31 或 CD34 检测到的微血管密度（MVD）的测量。然而，大多数可检索的公共数据通常由苏木精和伊红（H&E）染色的病理图像组成，很难获得相应的免疫组化图像。由于其复杂性和异质性，H&E 染色图像中小血管的作用尚未得到广泛研究。此外，由于手动识别微血管对于病理学家来说是一项劳动强度大的任务，因此存在很高的观察者间和观察者内变异性。因此，为了进行临床关联分析，在 H&E 染色的病理图像中开发自动化的微血管检测算法非常重要。
结果
本文提出了一种使用全卷积神经网络的微血管预测方法。通过对 H&E 染色图像的实验结果证明了我们提出的算法的可行性。此外，所识别的微血管特征与患者的临床结局显著相关。
结论
这是第一项开发用于 H&E 染色病理图像中自动化微血管检测的算法的研究。
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