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Application of the computer vision system for evaluation of pathomorphological images

机译:计算机视觉系统在病理形态学图像评估中的应用

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Work of medical personal with images is one of sought-after skill with insufficient amount of specialists that realized in their overloading and possible. In that connection purpose of our work was implementation of the computer vision system for evaluation of pathomorphological images as pathologists are most scarce specialist in modern medicine. We performed programmed and manual study of pathomorpological slides (immunohistochemical and cytological) with application of machine vision systems for counting of selected objects and comparison with previously manual estimation. The software was written in the Python 2.7 programming language using the OpenCV library for other purposes was modified. Two features were used to determine the nuclei and cells: the characteristic color range and the ratio of the area of the object to the square of its perimeter. We obtain average relative error of the suggested soft version about 9.2%, so accuracy of detection of cancer markers is 90.8% that is sufficient for the initial examination of a patient with screening examination of large number of patients even in so difficult images as immunohistochemistry.
机译:具有图像的医务人员的工作是最受欢迎的技能之一,专家数量不足,他们无法实现超负荷工作并可能。在这方面,我们的工作目的是实施计算机视觉系统以评估病理形态学图像,因为病理学家是现代医学中最稀缺的专家。我们使用机器视觉系统对选定的对象进行计数并与先前的手动估计进行比较,对病理性载玻片(免疫组织化学和细胞学)进行了程序化和手动研究。出于其他目的,使用OpenCV库以Python 2.7编程语言编写了该软件。两个特征被用来确定细胞核和细胞:特征颜色范围和物体的面积与周长的平方之比。我们获得的建议软件版本的平均相对误差约为9.2%,因此,癌症标志物的检测准确度为90.8%,即使对于像免疫组织化学这样困难的图像,也能对大量患者进行筛查的初始检查就足够了。

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