ABSTRACT
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