Department of Biomedical Engineering, Shenzhen University, Shenzhen, 518060, China; National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Shenzhen, 518060, China; Guangdong Key Laboratory of Biomedical Information Detection and Ultrasound Imaging, Shenzhen, 518060, China.
Cytometry A. 2014 Mar;85(3):214-30. doi: 10.1002/cyto.a.22407. Epub 2013 Dec 20.
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 μm. 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.
当前用于宫颈癌筛查的自动化辅助技术主要依赖于具有专利染色的自动化液基细胞学载玻片。对于发展中国家来说，这种方法成本效益不高。在本文中，我们提出了第一个用于筛查手动液基细胞学（MLBC）载玻片上的宫颈癌的自动化辅助系统，该系统使用苏木精和伊红（H&E）染色，价格低廉，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，提出了一种自动对焦方案，通过迭代比较特定位置的图像质量来找到焦点曲线的全局最大值。在自动对焦图像上，对增强的 a*通道进行多路图割（GC）全局处理，以获得细胞质分割。细胞核，特别是异常核，通过自适应和局部 GC 稳健分割。整合了两种基于凹面的方法来分割粘连的细胞核。为了对分割的细胞进行分类，选择和预处理特征以提高灵敏度，并引入上下文和细胞质信息以提高特异性。在 26 个连续的图像堆栈上的实验表明，动态自动对焦精度为 2.06μm。在 21 张具有非理想成像条件和病理学的宫颈细胞图像上，我们的分割方法在细胞质方面达到了 93%的准确率，在细胞核方面达到了 87.3%的 F 度量，在准确率方面均优于现有技术。额外的临床试验表明，我们的系统的灵敏度（88.1%）和特异性（100%）都非常令人满意。这些结果证明了在 H&E 染色的 MLBC 载玻片上进行自动化辅助宫颈癌筛查的可行性，这在社区卫生中心和小医院中非常需要。
