Comparison of object detection approaches applied to field images of Papanicolaou stained cytology slides

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Abstract

Papanicolaou is an inexpensive and non-invasive method, generally applied to detect cervical cancer, that can also be useful to detect cancer on oral cavities. Although oral cancer is considered a global health issue with 350.000 people diagnosed over a year it can successfully be treated if diagnosed at early stages. The manual process of analyzing cells to detect abnormalities is time-consuming and subject to variations in perceptions from different professionals. To evaluate a possible solution to the automation of this process, in this paper we employ the object detection deep learning approach in the analysis of this type of image using 3 models: RetinaNet, Faster R-CNN, and Mask R-CNN. We trained and tested the models using images from 6 cytology slides (4 cancer cases and 2 healthy samples) and our results show that Mask R-CNN was the best model for localization and classification of nuclei with an IoU of 0.51 and recall of abnormal nuclei of 0.67.

Bibtex

@article{MatiasDetectionPAP2021,
    doi = {10.1101/2021.08.25.21262605},
    url = {https://doi.org/10.1101/2021.08.25.21262605},
    year = {2021},
    month = aug,
    publisher = {Cold Spring Harbor Laboratory},
    author = {Andr{\'{e}} Vict{\'{o}}ria Matias and Allan Cerentini and Luiz Antonio Buschetto Macarini and Jo{\~{a}}o Gustavo Atkinson Amorim and Felipe Perozzo Dalto{\'{e}} and Aldo von Wangenheim},
    title = {Comparison of Object Detection Approaches Applied to Field Images of Papanicolaou Stained Cytology Slides}
}