ORCID Profile
0000-0001-6716-6732
Current Organisation
The University of Edinburgh
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Publisher: Cold Spring Harbor Laboratory
Date: 31-01-2021
DOI: 10.1101/2021.01.26.21250284
Abstract: Microscopic examination of blood smears remains the gold standard for diagnosis and laboratory studies with malaria. Inspection of smears is, however, a tedious manual process dependent on trained microscopists with results varying in accuracy between in iduals, given the heterogeneity of parasite cell form and disagreement on nomenclature. To address this, we sought to develop an automated image analysis method that improves accuracy and standardisation of cytological smear inspection but retains the capacity for expert confirmation and archiving of images. Here we present a machine-learning method that achieves red blood cell (RBC) detection, differentiation between infected and uninfected RBCs and parasite life stage categorisation from raw, unprocessed heterogeneous images of thin blood films. The method uses a pre-trained Faster Region-Based Convolutional Neural Networks (R-CNN) model for RBC detection that performs accurately, with an average precision of 0.99 at an intersection-over-union threshold of 0.5. A residual neural network (ResNet)-50 model applied to detect infection in segmented RBCs also performs accurately, with an area under the receiver operating characteristic curve of 0.98. Lastly, using a regression model our method successfully recapitulates intra-erythrocytic developmental cycle (IDC) stages with accurate categorisation (ring, trophozoite, schizont), as well as differentiating asexual stages from gametocytes. To accelerate our method’s utility, we have developed a mobile-friendly web-based interface, PlasmoCount, which is capable of automated detection and staging of malaria parasites from uploaded heterogeneous input images of Giemsa-stained thin blood smears. Results gained using either laboratory or phone-based images permit rapid navigation through and review of results for quality assurance. By standardising the assessment of parasite development from microscopic blood smears, PlasmoCount markedly improves user consistency and reproducibility and thereby presents a realistic route to automating the gold standard of field-based malaria diagnosis. Microscopy inspection of Giemsa-stained thin blood smears on glass slides has been used in the diagnosis of malaria and monitoring of malaria cultures in laboratory settings for years. Manual evaluation is, however, time-consuming, error-prone and subjective with no currently available tool that permits reliable automated counting and archiving of Giemsa-stained images. Here, we present a machine learning method for automated detection and staging of parasite infected red cells from heterogeneous smears. Our method calculates parasitaemia and frequency data on the malaria parasite intraerythrocytic development cycle directly from raw images, standardizing smear assessment and providing reproducible and archivable results. Developed into a web tool, PlasmoCount, this method provides improved standardisation of smear inspection for malaria research and potentially field diagnosis.
Publisher: Springer Science and Business Media LLC
Date: 23-03-2011
DOI: 10.1038/NATURE09831
Publisher: Elsevier BV
Date: 12-2007
Publisher: Springer Science and Business Media LLC
Date: 05-2008
DOI: 10.1038/NATURE06954
Publisher: University of Chicago Press
Date: 03-2011
DOI: 10.1086/658175
Publisher: Cambridge University Press (CUP)
Date: 2021
DOI: 10.1017/S2633903X21000015
Abstract: Microscopic examination of blood smears remains the gold standard for laboratory inspection and diagnosis of malaria. Smear inspection is, however, time-consuming and dependent on trained microscopists with results varying in accuracy. We sought to develop an automated image analysis method to improve accuracy and standardization of smear inspection that retains capacity for expert confirmation and image archiving. Here, we present a machine learning method that achieves red blood cell (RBC) detection, differentiation between infected/uninfected cells, and parasite life stage categorization from unprocessed, heterogeneous smear images. Based on a pretrained Faster Region-Based Convolutional Neural Networks (R-CNN) model for RBC detection, our model performs accurately, with an average precision of 0.99 at an intersection-over-union threshold of 0.5. Application of a residual neural network-50 model to infected cells also performs accurately, with an area under the receiver operating characteristic curve of 0.98. Finally, combining our method with a regression model successfully recapitulates intraerythrocytic developmental cycle with accurate lifecycle stage categorization. Combined with a mobile-friendly web-based interface, called PlasmoCount, our method permits rapid navigation through and review of results for quality assurance. By standardizing assessment of Giemsa smears, our method markedly improves inspection reproducibility and presents a realistic route to both routine lab and future field-based automated malaria diagnosis.
Publisher: Wiley
Date: 10-10-2012
DOI: 10.1111/EVA.12005
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Sarah Reece.