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Automated estimation of parasitaemia of Plasmodium yoelii-infected mice by digital image analysis of Giemsa-stained thin blood smears

BACKGROUND: Parasitaemia, the percentage of infected erythrocytes, is used to measure progress of experimental Plasmodium infection in infected hosts. The most widely used technique for parasitaemia determination is manual microscopic enumeration of Giemsa-stained blood films. This process is onerou...

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Autores principales: Ma, Charles, Harrison, Paul, Wang, Lina, Coppel, Ross L
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3245511/
https://www.ncbi.nlm.nih.gov/pubmed/21122144
http://dx.doi.org/10.1186/1475-2875-9-348
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author Ma, Charles
Harrison, Paul
Wang, Lina
Coppel, Ross L
author_facet Ma, Charles
Harrison, Paul
Wang, Lina
Coppel, Ross L
author_sort Ma, Charles
collection PubMed
description BACKGROUND: Parasitaemia, the percentage of infected erythrocytes, is used to measure progress of experimental Plasmodium infection in infected hosts. The most widely used technique for parasitaemia determination is manual microscopic enumeration of Giemsa-stained blood films. This process is onerous, time consuming and relies on the expertise of the experimenter giving rise to person-to-person variability. Here the development of image-analysis software, named Plasmodium AutoCount, which can automatically generate parasitaemia values from Plasmodium-infected blood smears, is reported. METHODS: Giemsa-stained blood smear images were captured with a camera attached to a microscope and analysed using a programme written in the Python programming language. The programme design involved foreground detection, cell and infection detection, and spurious hit filtering. A number of parameters were adjusted by a calibration process using a set of representative images. Another programme, Counting Aid, written in Visual Basic, was developed to aid manual counting when the quality of blood smear preparation is too poor for use with the automated programme. RESULTS: This programme has been validated for use in estimation of parasitemia in mouse infection by Plasmodium yoelii and used to monitor parasitaemia on a daily basis for an entire challenge infection. The parasitaemia values determined by Plasmodium AutoCount were shown to be highly correlated with the results obtained by manual counting, and the discrepancy between automated and manual counting results were comparable to those found among manual counts of different experimenters. CONCLUSIONS: Plasmodium AutoCount has proven to be a useful tool for rapid and accurate determination of parasitaemia from infected mouse blood. For greater accuracy when smear quality is poor, Plasmodium AutoCount, can be used in conjunction with Counting Aid.
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spelling pubmed-32455112011-12-27 Automated estimation of parasitaemia of Plasmodium yoelii-infected mice by digital image analysis of Giemsa-stained thin blood smears Ma, Charles Harrison, Paul Wang, Lina Coppel, Ross L Malar J Research BACKGROUND: Parasitaemia, the percentage of infected erythrocytes, is used to measure progress of experimental Plasmodium infection in infected hosts. The most widely used technique for parasitaemia determination is manual microscopic enumeration of Giemsa-stained blood films. This process is onerous, time consuming and relies on the expertise of the experimenter giving rise to person-to-person variability. Here the development of image-analysis software, named Plasmodium AutoCount, which can automatically generate parasitaemia values from Plasmodium-infected blood smears, is reported. METHODS: Giemsa-stained blood smear images were captured with a camera attached to a microscope and analysed using a programme written in the Python programming language. The programme design involved foreground detection, cell and infection detection, and spurious hit filtering. A number of parameters were adjusted by a calibration process using a set of representative images. Another programme, Counting Aid, written in Visual Basic, was developed to aid manual counting when the quality of blood smear preparation is too poor for use with the automated programme. RESULTS: This programme has been validated for use in estimation of parasitemia in mouse infection by Plasmodium yoelii and used to monitor parasitaemia on a daily basis for an entire challenge infection. The parasitaemia values determined by Plasmodium AutoCount were shown to be highly correlated with the results obtained by manual counting, and the discrepancy between automated and manual counting results were comparable to those found among manual counts of different experimenters. CONCLUSIONS: Plasmodium AutoCount has proven to be a useful tool for rapid and accurate determination of parasitaemia from infected mouse blood. For greater accuracy when smear quality is poor, Plasmodium AutoCount, can be used in conjunction with Counting Aid. BioMed Central 2010-12-01 /pmc/articles/PMC3245511/ /pubmed/21122144 http://dx.doi.org/10.1186/1475-2875-9-348 Text en Copyright ©2010 Ma et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<url>http://creativecommons.org/licenses/by/2.0</url>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research
Ma, Charles
Harrison, Paul
Wang, Lina
Coppel, Ross L
Automated estimation of parasitaemia of Plasmodium yoelii-infected mice by digital image analysis of Giemsa-stained thin blood smears
title Automated estimation of parasitaemia of Plasmodium yoelii-infected mice by digital image analysis of Giemsa-stained thin blood smears
title_full Automated estimation of parasitaemia of Plasmodium yoelii-infected mice by digital image analysis of Giemsa-stained thin blood smears
title_fullStr Automated estimation of parasitaemia of Plasmodium yoelii-infected mice by digital image analysis of Giemsa-stained thin blood smears
title_full_unstemmed Automated estimation of parasitaemia of Plasmodium yoelii-infected mice by digital image analysis of Giemsa-stained thin blood smears
title_short Automated estimation of parasitaemia of Plasmodium yoelii-infected mice by digital image analysis of Giemsa-stained thin blood smears
title_sort automated estimation of parasitaemia of plasmodium yoelii-infected mice by digital image analysis of giemsa-stained thin blood smears
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3245511/
https://www.ncbi.nlm.nih.gov/pubmed/21122144
http://dx.doi.org/10.1186/1475-2875-9-348
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