DeepAI AI Chat
Log In Sign Up

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

by   Luiz Antonio Buschetto Macarini, et al.

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:


page 3

page 4

page 5


A Deep Learning based Pipeline for Efficient Oral Cancer Screening on Whole Slide Images

Oral cancer incidence is rapidly increasing worldwide. The most importan...

AxonDeepSeg: automatic axon and myelin segmentation from microscopy data using convolutional neural networks

Segmentation of axon and myelin from microscopy images of the nervous sy...

Acute Lymphoblastic Leukemia Classification from Microscopic Images using Convolutional Neural Networks

Examining blood microscopic images for leukemia is necessary when expens...

A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images

Colorectal cancer (CRC) is the third cause of cancer death worldwide. Cu...

Enhanced Optic Disk and Cup Segmentation with Glaucoma Screening from Fundus Images using Position encoded CNNs

In this manuscript, we present a robust method for glaucoma screening fr...

Detection of Diabetic Anomalies in Retinal Images using Morphological Cascading Decision Tree

This research aims to develop an efficient system for screening of diabe...