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首页> 外文期刊>Cytometry, Part A: the journal of the International Society for Analytical Cytology >Automatic segmentation of cell nuclei in Feulgen-stained histological sections of prostate cancer and quantitative evaluation of segmentation results
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Automatic segmentation of cell nuclei in Feulgen-stained histological sections of prostate cancer and quantitative evaluation of segmentation results

机译：前列腺癌的Feulgen染色组织切片中细胞核的自动分割和分割结果的定量评估

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摘要

Digital image analysis of cell nuclei is useful to obtain quantitative information for the diagnosis and prognosis of cancer. However, the lack of a reliable automatic nuclear segmentation is a limiting factor for high-throughput nuclear image analysis. We have developed a method for automatic segmentation of nuclei in Feulgen-stained histological sections of prostate cancer. A local adaptive thresholding with an object perimeter gradient verification step detected the nuclei and was combined with an active contour model that featured an optimized initialization and worked within a restricted region to improve convergence of the segmentation of each nucleus. The method was tested on 30 randomly selected image frames from three cases, comparing the results from the automatic algorithm to a manual delineation of 924 nuclei. The automatic method segmented a few more nuclei compared to the manual method, and about 73% of the manually segmented nuclei were also segmented by the automatic method. For each nucleus segmented both manually and automatically, the accuracy (i.e., agreement with manual delineation) was estimated. The mean segmentation sensitivity/specificity were 95%/96%. The results from the automatic method were not significantly different from the ground truth provided by manual segmentation. This opens the possibility for large-scale nuclear analysis based on automatic segmentation of nuclei in Feulgen-stained histological sections.
机译：细胞核的数字图像分析可用于获得定量信息，以诊断和预测癌症。然而，缺乏可靠的自动核分割是高通量核图像分析的限制因素。我们已经开发了一种在Feulgen染色的前列腺癌组织切片中自动分割细胞核的方法。带有对象周边梯度验证步骤的局部自适应阈值检测到了原子核，并与主动轮廓模型相结合，该轮廓模型具有优化的初始化功能，并在受限区域内工作，以改善每个原子核分割的收敛性。该方法在3种情况下的30个随机选择的图像帧上进行了测试，将自动算法的结果与924个核的手动描绘进行了比较。与手动方法相比，自动方法分割了更多的核，并且自动方法也分割了约73％的手动分割核。对于手动和自动分割的每个原子核，其准确度（即与手动描绘的一致性）都得到了估算。平均分割敏感性/特异性为95％/ 96％。自动方法的结果与手动分割提供的基本事实没有显着差异。这为基于Feulgen染色的组织切片中细胞核的自动分割进行大规模核分析提供了可能性。

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