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ARAM: an automated image analysis software to determine rosetting parameters and parasitaemia in Plasmodium samples
BACKGROUND: Rosetting is associated with severe malaria and a primary cause of death in Plasmodium falciparum infections. Detailed understanding of this adhesive phenomenon may enable the development of new therapies interfering with rosette formation. For this, it is crucial to determine parameters...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4835829/ https://www.ncbi.nlm.nih.gov/pubmed/27090910 http://dx.doi.org/10.1186/s12936-016-1243-4 |
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author | Kudella, Patrick Wolfgang Moll, Kirsten Wahlgren, Mats Wixforth, Achim Westerhausen, Christoph |
author_facet | Kudella, Patrick Wolfgang Moll, Kirsten Wahlgren, Mats Wixforth, Achim Westerhausen, Christoph |
author_sort | Kudella, Patrick Wolfgang |
collection | PubMed |
description | BACKGROUND: Rosetting is associated with severe malaria and a primary cause of death in Plasmodium falciparum infections. Detailed understanding of this adhesive phenomenon may enable the development of new therapies interfering with rosette formation. For this, it is crucial to determine parameters such as rosetting and parasitaemia of laboratory strains or patient isolates, a bottleneck in malaria research due to the time consuming and error prone manual analysis of specimens. Here, the automated, free, stand-alone analysis software automated rosetting analyzer for micrographs (ARAM) to determine rosetting rate, rosette size distribution as well as parasitaemia with a convenient graphical user interface is presented. METHODS: Automated rosetting analyzer for micrographs is an executable with two operation modes for automated identification of objects on images. The default mode detects red blood cells and fluorescently labelled parasitized red blood cells by combining an intensity-gradient with a threshold filter. The second mode determines object location and size distribution from a single contrast method. The obtained results are compared with standardized manual analysis. Automated rosetting analyzer for micrographs calculates statistical confidence probabilities for rosetting rate and parasitaemia. RESULTS: Automated rosetting analyzer for micrographs analyses 25 cell objects per second reliably delivering identical results compared to manual analysis. For the first time rosette size distribution is determined in a precise and quantitative manner employing ARAM in combination with established inhibition tests. Additionally ARAM measures the essential observables parasitaemia, rosetting rate and size as well as location of all detected objects and provides confidence intervals for the determined observables. No other existing software solution offers this range of function. The second, non-malaria specific, analysis mode of ARAM offers the functionality to detect arbitrary objects. CONCLUSIONS: Automated rosetting analyzer for micrographs has the capability to push malaria research to a more quantitative and statistically significant level with increased reliability due to operator independence. As an installation file for Windows © 7, 8.1 and 10 is available for free, ARAM offers a novel open and easy-to-use platform for the malaria community to elucidate rosetting. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12936-016-1243-4) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-4835829 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-48358292016-04-20 ARAM: an automated image analysis software to determine rosetting parameters and parasitaemia in Plasmodium samples Kudella, Patrick Wolfgang Moll, Kirsten Wahlgren, Mats Wixforth, Achim Westerhausen, Christoph Malar J Methodology BACKGROUND: Rosetting is associated with severe malaria and a primary cause of death in Plasmodium falciparum infections. Detailed understanding of this adhesive phenomenon may enable the development of new therapies interfering with rosette formation. For this, it is crucial to determine parameters such as rosetting and parasitaemia of laboratory strains or patient isolates, a bottleneck in malaria research due to the time consuming and error prone manual analysis of specimens. Here, the automated, free, stand-alone analysis software automated rosetting analyzer for micrographs (ARAM) to determine rosetting rate, rosette size distribution as well as parasitaemia with a convenient graphical user interface is presented. METHODS: Automated rosetting analyzer for micrographs is an executable with two operation modes for automated identification of objects on images. The default mode detects red blood cells and fluorescently labelled parasitized red blood cells by combining an intensity-gradient with a threshold filter. The second mode determines object location and size distribution from a single contrast method. The obtained results are compared with standardized manual analysis. Automated rosetting analyzer for micrographs calculates statistical confidence probabilities for rosetting rate and parasitaemia. RESULTS: Automated rosetting analyzer for micrographs analyses 25 cell objects per second reliably delivering identical results compared to manual analysis. For the first time rosette size distribution is determined in a precise and quantitative manner employing ARAM in combination with established inhibition tests. Additionally ARAM measures the essential observables parasitaemia, rosetting rate and size as well as location of all detected objects and provides confidence intervals for the determined observables. No other existing software solution offers this range of function. The second, non-malaria specific, analysis mode of ARAM offers the functionality to detect arbitrary objects. CONCLUSIONS: Automated rosetting analyzer for micrographs has the capability to push malaria research to a more quantitative and statistically significant level with increased reliability due to operator independence. As an installation file for Windows © 7, 8.1 and 10 is available for free, ARAM offers a novel open and easy-to-use platform for the malaria community to elucidate rosetting. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12936-016-1243-4) contains supplementary material, which is available to authorized users. BioMed Central 2016-04-18 /pmc/articles/PMC4835829/ /pubmed/27090910 http://dx.doi.org/10.1186/s12936-016-1243-4 Text en © Kudella et al. 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Methodology Kudella, Patrick Wolfgang Moll, Kirsten Wahlgren, Mats Wixforth, Achim Westerhausen, Christoph ARAM: an automated image analysis software to determine rosetting parameters and parasitaemia in Plasmodium samples |
title | ARAM: an automated image analysis software to determine rosetting parameters and parasitaemia in Plasmodium samples |
title_full | ARAM: an automated image analysis software to determine rosetting parameters and parasitaemia in Plasmodium samples |
title_fullStr | ARAM: an automated image analysis software to determine rosetting parameters and parasitaemia in Plasmodium samples |
title_full_unstemmed | ARAM: an automated image analysis software to determine rosetting parameters and parasitaemia in Plasmodium samples |
title_short | ARAM: an automated image analysis software to determine rosetting parameters and parasitaemia in Plasmodium samples |
title_sort | aram: an automated image analysis software to determine rosetting parameters and parasitaemia in plasmodium samples |
topic | Methodology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4835829/ https://www.ncbi.nlm.nih.gov/pubmed/27090910 http://dx.doi.org/10.1186/s12936-016-1243-4 |
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