Author Affiliations
Abstract
1 Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong, University of Science and Technology, Wuhan, Hubei 430074, P. R. China
2 MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China
3 Department of Clinical Laboratory, Tongji Hospital, Huazhong University of Science and Technology, Wuhan, Hubei 430030, P. R. China
4 Department of Pathology, Hubei Maternal and Child Health Hospital, Wuhan, Hubei 430072, P. R. China
Computer-assisted cervical screening is an effective method to save the doctors' workload and improve their work e±ciency. 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.
Cervical cancer instance segmentation nucleus classification lesion cognition Journal of Innovative Optical Health Sciences
2020, 13(1):