基于强度轮廓的数字组织学图像重叠细胞核分割

Md. Shamim Hossain, L. Armstrong, Jumana Abu-Khalaf, David M. Cook, P. Zaenker
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引用次数: 0

摘要

组织病理学图像分析中的自动核分割技术不断改进。机器学习模型需要对大型数据集进行注释,这是一个耗时、昂贵且费力的过程。这种分割在检测接触核或重叠核时也受到限制,并将任何重叠核视为单个核。这是由于低对比度的图像,遮挡,和细胞核的多样性。为了解决这些问题,本文提出了一种基于U-net和基于强度的轮廓技术的自动重叠核分割模型。在之前的研究中,使用合成数据训练U-net分割模型,该模型使用GAN模型生成,其中使用少量组织病理学数据生成合成数据。这减少了深度学习模型中的数据限制和对核注释的需求。在本研究中,网络最初没有考虑重叠的核区域进行分割。因此,提出了一种基于强度的等高线来分离重叠核区域。利用距离变换来确定每个原子核的中心。首先进行局部极小值的识别,然后进行基于强度的梯度权重,得到重叠核的最终分割线。对重叠核的边界进行了细化,并去除了噪声,以便清晰地描述每个核区域。与现有方法相比,该方法分离重叠核的准确率为91.6%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Overlapping Cell Nuclei Segmentation in Digital Histology Images using Intensity-based Contours
Automated nuclei segmentation techniques in histopathological image analysis continue to improve. The machine learning model requires the annotation of large data sets which is a time-consuming, expensive, and laborious process. This segmentation is also limited in detecting touching or overlapping nuclei and considers any overlapping nuclei as a single nucleus. This is due to low contrast images, occultation, and diversity of cell nuclei. This work proposes an automated overlapping nuclei segmentation model with a U-net and an intensity-based contour technique in order to address these issues. In a previous study, a U-net segmentation model was trained with synthetic data, which was generated using a GAN model, where a small number of histopathology data was used to generate the synthetic data. This reduced the data limitation and need for nuclei annotation in the deep learning model. Initially in this study, the overlapping nuclei regions were not considered for segmentation by the network. Hence, an intensity-based contour line is proposed to separate overlapping nuclei regions. The distance transformation is utilized to define the center of each nucleus. The identification of local minima followed by intensity-based gradient weights is applied to obtain the final segmentation line of overlapping nuclei. The boundary of the overlapping nuclei is refined, and noise is removed in order to clearly describe each nuclei region. The proposed method results in 91.6% accuracy in separating the overlapping nuclei compared to other existing methods.
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