Stenkvist B, Strande G
Department of Pathology, Karolinska Institute and Hospital, Stockholm, Sweden.
Anal Cell Pathol. 1989 Dec;2(1):1-13.
An analysis has been performed of visual diagnostic criteria used in cervical cytology applied to machine selected cells in relation to automated classification based on variables, which can be recorded in an image system with automated cell search and segmentation, feature extraction and classification. A 98% accuracy could be obtained with the choice of the most ideal statistical methods for discrimination and the use of the most powerful variables recorded in the image system when compared with consensus of the visual diagnoses based on established cytological criteria for diagnosis of cancer and precancer of the cervix uteri. The most powerful discriminatory variables in the image system (of 17 recorded) for discrimination between normal and abnormal epithelial cells were, in addition to nuclear extinction, cytoplasmic extinction and cytoplasmic shape. It is concluded that the visual classification of cervical cells is highly accurate with experienced observers and that imaging microscopes can be trained to nearly equal this accuracy with appropriate statistical methods of discrimination. The problem of creating fully automated systems, however, also requires the inclusion of even more effective discriminatory variables and also the solution of such problems as automatic cell search, segmentation, artifact rejection, feature extraction, classification and electronic stability in order to become cost-effective.
对宫颈细胞学中用于机器选择细胞的视觉诊断标准进行了分析，这些标准与基于变量的自动分类相关，这些变量可在具有自动细胞搜索和分割、特征提取及分类功能的图像系统中记录。与基于子宫颈癌和癌前病变的既定细胞学诊断标准的视觉诊断共识相比，选择最理想的判别统计方法并使用图像系统中记录的最有效变量时，可获得98%的准确率。图像系统中（记录的17个变量中）用于区分正常和异常上皮细胞的最有效判别变量，除了核消光外，还有细胞质消光和细胞质形状。结论是，经验丰富的观察者对宫颈细胞的视觉分类非常准确，并且通过适当的判别统计方法，成像显微镜可以训练到几乎与之相当的准确率。然而，创建全自动系统的问题还需要纳入更有效的判别变量，以及解决诸如自动细胞搜索、分割、伪像排除、特征提取、分类和电子稳定性等问题，以便具有成本效益。
