Computer-assisted cervical screening is an effective method to save the doctors’ workload and improve their work efficiency. Usually, the correct classification of cervical cells depends on the nuclear segmentation effect and the extraction of nuclear features. However, the precise nucleus segmentation remains a huge challenge, especially for densely distributed nucleus. Moreover, previous cellular classification methods are mostly based on morphological features of nucleus size or color. Those individual features can make accurate classification for severe lesions, but not for mild lesions. In this paper, we propose an accurate instance segmentation algorithm and propose cognition-based features to identify cervical cancer cells. Different from previous individual nucleus features, we also propose population features and cognition-based features according to the Bethesda System (TBS) for reporting cervical cytology and the diagnostic experience of the cytologists. The results showed that the segmentation achieves better success in complex situations than that by traditional segmentation algorithms. Besides, the cell classification via cognition-based features also help us find out more about less severe lesions’ nuclei than that based on conventional features of individual nucleus, meaning an improvement of classification accuracy for cervical screening.

中文翻译：

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通过基于认知的特征识别宫颈癌细胞

计算机辅助宫颈筛查是节省医生工作量、提高工作效率的有效方法。通常，宫颈细胞的正确分类取决于核分割效果和核特征的提取。然而，精确的细胞核分割仍然是一个巨大的挑战，特别是对于密集分布的细胞核。此外，以往的细胞分类方法大多基于细胞核大小或颜色的形态特征。这些个体特征可以对严重病变进行准确分类，但不能对轻度病变进行分类。在本文中，我们提出了一种准确的实例分割算法，并提出了基于认知的特征来识别宫颈癌细胞。不同于以往的单个核特征，我们还根据贝塞斯达系统 (TBS) 提出了人口特征和基于认知的特征，用于报告宫颈细胞学和细胞学家的诊断经验。结果表明，与传统的分割算法相比，该分割在复杂情况下取得了更好的成功。此外，基于认知特征的细胞分类也比基于单个细胞核的常规特征更能帮助我们找到不太严重的病变细胞核，这意味着宫颈筛查的分类准确性有所提高。