Towards a Complete Pipeline for Segmenting Nuclei in Feulgen-Stained Images

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 d...

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Published inarXiv.org
Main Authors Luiz Antonio Buschetto Macarini, Aldo von Wangenheim, Felipe Perozzo Daltoé, Alexandre Sherlley Casimiro Onofre, Fabiana Botelho de Miranda Onofre, Stemmer, Marcelo Ricardo
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 19.02.2020
Subjects
Artificial neural networks
Cancer
Image segmentation
Medical imaging
Nuclei (cytology)
Post-processing
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Summary: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.
ISSN:2331-8422