Investigation on Parameter Effect for Semi-automatic Contour Detection in Histopathological Image Processing

C. Stoean, R. Stoean, Adrian Sandita, C. Mesina, D. Ciobanu, C. Gruia
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引用次数: 7

Abstract

Histopathological image understanding is a demanding task for pathologists, involving the risky decision of confirming or denying the presence of cancer. What is more, the increased incidence of the disease, on the one hand, and the current prevention screening, on the other, result in an immense quantity of such pictures. For the colorectal cancer type in particular, a computational approach attempts to learn from small manually annotated portions of images and extend the findings to the complete ones. As the output of such techniques highly depends on the input variables, the current study conducts an investigation of the effect on the automatic contour detection that the choices for parameter values have from a cropped section to the complete image.
组织病理学图像处理中半自动轮廓检测参数效应研究
对病理学家来说,组织病理学图像理解是一项艰巨的任务,涉及确认或否认癌症存在的风险决策。更重要的是,一方面,疾病发病率的增加,另一方面,目前的预防筛查,导致了大量这样的照片。特别是对于结肠直肠癌类型,一种计算方法试图从图像的小部分人工注释中学习,并将发现扩展到完整的图像。由于这些技术的输出高度依赖于输入变量,因此本研究研究了从裁剪部分到完整图像参数值的选择对自动轮廓检测的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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