White Blood Cell Segmentation Using Fully Convolutional Neural Networks

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F18%3APU129510" target="_blank" >RIV/00216305:26220/18:PU129510 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
White Blood Cell Segmentation Using Fully Convolutional Neural Networks
Original language description
In medicine, the identification and counting of white blood cells are used for diagnosing diseases like inflammation, malignancy or leukaemia. In this paper, we propose a novel approach to white blood cell segmentation. On two different white blood cell datasets, two networks, PSPNet and U-Net are trained to perform simultaneous nucleus and cytoplasm segmentation. Compared to ground truth, our segmentations are almost identical, with smoother borders. When comparing overall cell segmentation with current methods, our networks are achieving similar (or better) results in evaluated metrics, with intersection over union reaching around 0.95 for both networks. DICE coefficient is higher than 0.96 for both networks and both datasets, which is a promising result of the segmentation.
Czech name
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Czech description
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Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical
Elektrorevue - Internetový časopis (http://www.elektrorevue.cz)
ISSN
1213-1539
e-ISSN
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Volume of the periodical
20
Issue of the periodical within the volume
5
Country of publishing house
CZ - CZECH REPUBLIC
Number of pages
8
Pages from-to
1-9
UT code for WoS article
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EID of the result in the Scopus database
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