  Cervical cancer is the second most common cancer type in women around the
world. In some countries, due to non-existent or inadequate screening, it is
often detected at late stages, making standard treatment options often absent
or unaffordable. It is a deadly disease that could benefit from early detection
approaches. It is usually done by cytological exams which consist of visually
inspecting the nuclei searching for morphological alteration. Since it is done
by humans, naturally, some subjectivity is introduced. Computational methods
could be used to reduce this, where the first stage of the process would be the
nuclei segmentation. In this context, we present a complete pipeline for the
segmentation of nuclei in Feulgen-stained images using Convolutional Neural
Networks. Here we show the entire process of segmentation, since the collection
of the samples, passing through pre-processing, training the network,
post-processing and results evaluation. We achieved an overall IoU of 0.78,
showing the affordability of the approach of nuclei segmentation on
Feulgen-stained images. The code is available in:
https://github.com/luizbuschetto/feulgen_nuclei_segmentation.
