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HESKO, B. HARABIŠ, V. KRÁLÍK, M.
Original Title
White Blood Cell Segmentation Using Fully Convolutional Neural Networks
Type
journal article - other
Language
English
Original Abstract
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.
Keywords
White blood cell, segmentation, deep learning, convolutional neural networks
Authors
HESKO, B.; HARABIŠ, V.; KRÁLÍK, M.
Released
31. 10. 2018
ISBN
1213-1539
Periodical
Elektrorevue - Internetový časopis (http://www.elektrorevue.cz)
Year of study
20
Number
5
State
Czech Republic
Pages from
1
Pages to
9
Pages count
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" }