Segmentation, Detection and Classification of Cell Nuclei on Oral Cytology Samples Stained with Papanicolaou

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Abstract

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. Papanicolaou is an unexpensive and non-invasive method, generally applied to detect cervical cancer, but it can also be useful to detect cancer on oral cavities. The manual process of analyzing cells to detect abnormalities is a time-consuming cell analysis and is subject to variations in perceptions from different professionals. This paper compares models for three different deep learning approaches: segmentation, object detection and image classification. Our results show that the binary object detection with Faster R-CNN is the best approach for nuclei detection and localization (0.76 IoU). Since ResNet 34 had a good performance on abnormal nuclei classification (0.88 accuracy, 0.86 F_1 score) we concluded that these two models can be used in combination to make a localization and classification pipeline. This work reinforces that the automated analysis of oral cytology to make a pipeline for nuclei classification and localization using deep learning can help to minimize the subjectivity of the human analysis and also to detect cancer at early stages.

Bibtex

@inproceedings{MatiasMultimethodPAP2020,
    doi = {10.1109/cbms49503.2020.00018},
    url = {https://doi.org/10.1109/cbms49503.2020.00018},
    year = {2020},
    month = jul,
    publisher = ,
    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 = {Segmentation,  Detection and Classification of Cell Nuclei on Oral Cytology Samples Stained with Papanicolaou},
    booktitle = {2020 {IEEE} 33rd International Symposium on Computer-Based Medical Systems ({CBMS})}
}