  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).
