2020/02/19 by Luiz Antonio Buschetto Macarini, Macarini, Luiz Antonio Buschetto, Aldo von Wangenheim +9
Computer Science · Engineering · Medicine · #AI in cancer detection #Artificial intelligence #Biology #Cervical Cancer and HPV Research #Code (set theory) #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Computer vision #Context (archaeology) #Convolutional neural network #FOS: Computer and information sciences #FOS: Electrical engineering #Feulgen stain #Image and Video Processing (eess.IV) #Market segmentation #Medicine #Paleontology #Pathology #Pattern recognition (psychology) #Pipeline (software) #Process (computing) #Radiomics and Machine Learning in Medical Imaging #Segmentation #Staining #cs.CV #eess.IV #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2002.08331
published in arXiv (Cornell University) (Cornell University) · 7 pages, 8 figures (Figure 2 with "a" and "b"), conference paper accepted for presentation in XI Computer on the Beach (https://www.computeronthebeach.com.br/)
arxiv created 2020/02/19 · openalex publication_date 2020/02/19 · arxiv updated 2020/02/20 · openalex created_date 2022/07/26 · openalex updated_date 2026/08/04
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/feulgennucleisegmentation.