Gurcan Metin N, Tomaszewski John, Overton James A, Doyle Scott, Ruttenberg Alan, Smith Barry
Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA.
Department of Pathology and Anatomical Sciences, University at Buffalo, Buffalo, NY 14214, USA.
J Biomed Inform. 2017 Feb;66:129-135. doi: 10.1016/j.jbi.2016.12.006. Epub 2016 Dec 18.
Interoperability across data sets is a key challenge for quantitative histopathological imaging. There is a need for an ontology that can support effective merging of pathological image data with associated clinical and demographic data. To foster organized, cross-disciplinary, information-driven collaborations in the pathological imaging field, we propose to develop an ontology to represent imaging data and methods used in pathological imaging and analysis, and call it Quantitative Histopathological Imaging Ontology - QHIO. We apply QHIO to breast cancer hot-spot detection with the goal of enhancing reliability of detection by promoting the sharing of data between image analysts.
数据集之间的互操作性是定量组织病理学成像的关键挑战。需要一种本体论来支持病理图像数据与相关临床和人口统计学数据的有效合并。为了促进病理成像领域有组织的、跨学科的、信息驱动的合作，我们提议开发一种本体论来表示病理成像和分析中使用的成像数据和方法，并将其称为定量组织病理学成像本体论（QHIO）。我们将QHIO应用于乳腺癌热点检测，目的是通过促进图像分析师之间的数据共享来提高检测的可靠性。
