Comparative analysis of deep learning approaches for AgNOR-stained cytology samples interpretation

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Abstract

Cervical cancer is a public health problem, where the treatment has a better chance of success if detected early. The analysis is a manual process which is subject to a human error, so this paper provides a way to analyze argyrophilic nucleolar organizer regions (AgNOR) stained slide using deep learning approaches. Also, this paper compares models for instance and semantic detection approaches. Our results show that the semantic segmentation using U-Net with ResNet-18 or ResNet-34 as the backbone have similar results, and the best model shows an IoU for nucleus, cluster, and satellites of 0.83, 0.92, and 0.99 respectively. For instance segmentation, the Mask R-CNN using ResNet-50 performs better in the visual inspection and has a 0.61 of the IoU metric. We conclude that the instance segmentation and semantic segmentation models can be used in combination to make a cascade model able to select a nucleus and subsequently segment the nucleus and its respective nucleolar organizer regions (NORs).

Bibtex

@misc{AtkinsonSegVsOD,
  doi = {10.48550/ARXIV.2210.10641},
  url = {https://arxiv.org/abs/2210.10641},
  author = {Jo{\~{a}}o Gustavo Atkinson Amorim and Andr{\'{e}} Vict{\'{o}}ria Matias and Allan Cerentini and Luiz Antonio Buschetto Macarini and Alexandre Sherlley Onofre and Fabiana Botelho Onofre and Aldo von Wangenheim},
  title = {Comparative analysis of deep learning approaches for AgNOR-stained cytology samples interpretation},
  publisher = {arXiv},
  year = {2022},
  copyright = {Creative Commons Attribution 4.0 International}
}