Federal University of Piauí, Brazil; Federal University of Ceará, Brazil.
University of California, Berkeley, USA; Lawrence Berkeley National Laboratory, USA.
Comput Med Imaging Graph. 2019 Mar;72:13-21. doi: 10.1016/j.compmedimag.2019.01.003. Epub 2019 Jan 30.
Ninety years after its invention, the Pap test continues to be the most used method for the early identification of cervical precancerous lesions. In this test, the cytopathologists look for microscopic abnormalities in and around the cells, which is a time-consuming and prone to human error task. This paper introduces computational tools for cytological analysis that incorporate cell segmentation deep learning techniques. These techniques are capable of processing both free-lying and clumps of abnormal cells with a high overlapping rate from digitized images of conventional Pap smears. Our methodology employs a preprocessing step that discards images with a low probability of containing abnormal cells without prior segmentation and, therefore, performs faster when compared with the existing methods. Also, it ranks outputs based on the likelihood of the images to contain abnormal cells. We evaluate our methodology on an image database of conventional Pap smears from real scenarios, with 108 fields-of-view containing at least one abnormal cell and 86 containing only normal cells, corresponding to millions of cells. Our results show that the proposed approach achieves accurate results (MAP = 0.936), runs faster than existing methods, and it is robust to the presence of white blood cells, and other contaminants.
巴氏涂片检测发明 90 年后，至今仍是用于早期识别宫颈癌前病变的最常用方法。在这项检测中，细胞病理学家寻找细胞内和周围的微观异常，这是一项耗时且容易出错的任务。本文介绍了细胞学分析的计算工具，这些工具结合了细胞分割深度学习技术。这些技术能够处理来自传统巴氏涂片数字化图像的高重叠率的游离和成团的异常细胞。我们的方法采用预处理步骤，在没有先分割的情况下，丢弃异常细胞含量低的图像，因此与现有方法相比速度更快。此外，它还根据图像中包含异常细胞的可能性对输出进行排序。我们在真实场景的常规巴氏涂片图像数据库上评估了我们的方法，该数据库包含至少一个异常细胞的 108 个视场和仅包含正常细胞的 86 个视场，对应于数百万个细胞。我们的结果表明，所提出的方法能够实现准确的结果（MAP=0.936），比现有方法更快，并且对白细胞和其他污染物的存在具有鲁棒性。
