Automation-Assisted Cervical Cancer Screening in Manual Liquid-Based Cytology With Hematoxylin and Eosin Staining
Automation-Assisted Cervical Cancer Screening in Manual Liquid-Based Cytology With Hematoxylin and Eosin Staining
复制标题
苏木精和伊红染色手动液基细胞学自动化辅助宫颈癌筛查
DOI：
10.1002/cyto.a.22407
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发表时间：
2014-03-01
期刊：
影响因子：
3.7
通讯作者：
Chen, Siping
中科院分区：
文献类型：
--
作者：
Zhang, Ling;Kong, Hui;Chen, Siping
目前用于筛查宫颈癌的自动化辅助技术主要依赖于具有专有染色剂的自动化液基细胞学载玻片。在发展中国家，这不是一个具有成本效益的办法。在这篇文章中，我们提出了第一个自动辅助系统，以筛选宫颈癌手动液基细胞学（MLBC）载玻片与苏木精和伊红（H&E）染色，这是廉价的，更适用于发展中国家。该系统由三个主要模块组成：图像采集、细胞分割和细胞分类。首先，提出了一种自动聚焦方案，通过迭代地比较特定位置的图像质量来找到聚焦曲线的全局最大值。在自动聚焦图像上，对a* 通道增强图像全局执行多路图切割（GC）以获得细胞质分割。该算法自适应地、局部地分割出细胞核，特别是异常细胞核。两个基于凹的方法被集成来分裂接触的核。为了对分割的细胞进行分类，选择特征并进行预处理以提高灵敏度，并且引入上下文和细胞质信息以提高特异性。在26幅连续图像上的实验表明，动态自动聚焦精度为2.06 m.在21个具有非理想成像条件和病理的宫颈细胞图像上，我们的分割方法对细胞质的准确率达到93%，对细胞核的F-测量达到87.3%，在准确性方面均优于最先进的作品。进一步的临床试验表明，我们的系统的敏感性（88.1%）和特异性（100%）都是令人满意的高。这些结果证明了在MLBC载玻片上用H&E染色进行自动辅助宫颈癌筛查的可行性，这在社区卫生中心和小型医院是非常可取的。(c)2013年国际细胞计数促进学会
Current automation-assisted technologies for screening cervical cancer mainly rely on automated liquid-based cytology slides with proprietary stain. This is not a cost-efficient approach to be utilized in developing countries. In this article, we propose the first automation-assisted system to screen cervical cancer in manual liquid-based cytology (MLBC) slides with hematoxylin and eosin (H&E) stain, which is inexpensive and more applicable in developing countries. This system consists of three main modules: image acquisition, cell segmentation, and cell classification. First, an autofocusing scheme is proposed to find the global maximum of the focus curve by iteratively comparing image qualities of specific locations. On the autofocused images, the multiway graph cut (GC) is performed globally on the a* channel enhanced image to obtain cytoplasm segmentation. The nuclei, especially abnormal nuclei, are robustly segmented by using GC adaptively and locally. Two concave-based approaches are integrated to split the touching nuclei. To classify the segmented cells, features are selected and preprocessed to improve the sensitivity, and contextual and cytoplasm information are introduced to improve the specificity. Experiments on 26 consecutive image stacks demonstrated that the dynamic autofocusing accuracy was 2.06 m. On 21 cervical cell images with nonideal imaging condition and pathology, our segmentation method achieved a 93% accuracy for cytoplasm, and a 87.3% F-measure for nuclei, both outperformed state of the art works in terms of accuracy. Additional clinical trials showed that both the sensitivity (88.1%) and the specificity (100%) of our system are satisfyingly high. These results proved the feasibility of automation-assisted cervical cancer screening in MLBC slides with H&E stain, which is highly desirable in community health centers and small hospitals. (c) 2013 International Society for Advancement of Cytometry