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Deep learning for nuclei segmentation and cell classification in cervical liquid based cytology.
Liquid Based Cytology (LBC) is an effective technique for cervical cancer screening through the Papanicolaou (Pap) test. Currently, most LBC screening is done by cytologists, which is very time consuming and expensive. Reliable automated methods are needed to assist cytologists to quickly locate abnormal cells. State of the art in cell classification assumes that cells have already been segmented. However, clustered cells are very challenging to segment. We noticed that in contrast to cells, nuclei are relatively easier to segment, and according to The Bethesda System (TBS), the gold standard for cervical cytology reporting, cervical cytology abnormalities are often closely correlated with nucleus abnormalities. We propose a two-step algorithm, which avoids cell segmentation. We train a Mask R-CNN model to segment nuclei, and then classify cell patches centered at the segmented nuclei in roughly the size of a healthy cell. Evaluation with a dataset of 25 high resolution NDPI whole slide images shows that nuclei segmentation followed by cell patch classification is a promising approach to build practically useful automated Pap test applications.