Multiplication free neural network for cancer stem cell detection in H-and-E stained liver images
Multiplication free neural network for cancer stem cell detection in H-and-E stained liver images
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用于 H 和 E 染色肝脏图像中癌症干细胞检测的无乘法神经网络
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
A. Çetin
中科院分区：
文献类型：
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作者：
Diaa Badawi;Ece Akhan;Maen Mallah;A. Üner;R. Cetin;A. Çetin
CD13和CD133等标记物已被用于识别各种组织图像中的肿瘤干细胞(CSC)。在CD13染色的肝组织图像中，CSC核很可能呈现为棕色。我们观察到CD13图像中棕色/蓝色细胞核的比率与H&E染色肝脏图像中深蓝色/蓝色细胞核比率之间存在着高度的相关性。因此，我们建议，在HE染色的组织图像中观察到许多深蓝色核的病理学家也可以使用CD13染色来估计CSC比率。在本文中，我们描述了一种基于神经网络的计算机视觉方法，在H&E染色的肝组织图像中估计深蓝与蓝色核的比率。神经网络结构基于仅使用加法和符号运算的无乘法运算符。给出了实验结果。
Markers such as CD13 and CD133 have been used to identify Cancer Stem Cells (CSC) in various tissue images. It is highly likely that CSC nuclei appear as brown in CD13 stained liver tissue images. We observe that there is a high correlation between the ratio of brown to blue colored nuclei in CD13 images and the ratio between the dark blue to blue colored nuclei in H&E stained liver images. Therefore, we recommend that a pathologist observing many dark blue nuclei in an H&E stained tissue image may also order CD13 staining to estimate the CSC ratio. In this paper, we describe a computer vision method based on a neural network estimating the ratio of dark blue to blue colored nuclei in an H&E stained liver tissue image. The neural network structure is based on a multiplication free operator using only additions and sign operations. Experimental results are presented.