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Cervical cancer is a public health problem, where the treatment has a betterchance of success if detected early. The analysis is a manual process which issubject to a human error, so this paper provides a way to analyze argyrophilicnucleolar organizer regions (AgNOR) stained slide using deep learningapproaches. Also, this paper compares models for instance and semanticdetection approaches. Our results show that the semantic segmentation usingU-Net with ResNet-18 or ResNet-34 as the backbone have similar results, and thebest model shows an IoU for nucleus, cluster, and satellites of 0.83, 0.92, and0.99 respectively. For instance segmentation, the Mask R-CNN using ResNet-50performs better in the visual inspection and has a 0.61 of the IoU metric. Weconclude that the instance segmentation and semantic segmentation models can beused in combination to make a cascade model able to select a nucleus andsubsequently segment the nucleus and its respective nucleolar organizer regions(NORs).

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