Discrimination of cervical cancer cells via cognition-based features

Journal of Innovative Optical Health Sciences, Ahead of Print. 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. Mor eover, 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 prop ose 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 cytologist s. 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 fea tures of individual nucleus, meaning an improvement of classification accuracy for cervical screening.
Source: Journal of Innovative Optical Health Sciences - Category: Biomedical Science Authors: Source Type: research