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HESKO, B. HARABIŠ, V. KRÁLÍK, M.
Originální název
White Blood Cell Segmentation Using Fully Convolutional Neural Networks
Typ
článek v časopise - ostatní, Jost
Jazyk
angličtina
Originální abstrakt
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.
Klíčová slova
White blood cell, segmentation, deep learning, convolutional neural networks
Autoři
HESKO, B.; HARABIŠ, V.; KRÁLÍK, M.
Vydáno
31. 10. 2018
ISSN
1213-1539
Periodikum
Elektrorevue - Internetový časopis (http://www.elektrorevue.cz)
Ročník
20
Číslo
5
Stát
Česká republika
Strany od
1
Strany do
9
Strany počet
8
BibTex
@article{BUT150871, author="Branislav {Hesko} and Vratislav {Harabiš} and Martin {Králík}", title="White Blood Cell Segmentation Using Fully Convolutional Neural Networks", journal="Elektrorevue - Internetový časopis (http://www.elektrorevue.cz)", year="2018", volume="20", number="5", pages="1--9", issn="1213-1539" }