Computational protocol: Reliable enumeration of malaria parasites in thick blood films using digital image analysis

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Protocol publication

[…] Thick blood films, Giemsa-stained to uniform standards [] were obtained from our slide bank of malaria proficiency-testing specimens, or loaned from a similar collection (K. Lilley, Army Malaria Institute, Brisbane). Blood samples for malaria microscopy proficiency testing were collected and used with ethical approval of the Human Research Ethics Committee (Medical), University of the Witwatersrand, Johannesburg (protocol number M051126). Twenty films containing Plasmodium falciparum were selected to provide parasite densities ranging from 5,000 to 500,000 parasites/μl. These included eight re-sampled or duplicate slides to test reproducibility of methods both within and between films prepared from the same blood specimens. Parasite densities had been previously established by experienced microscopists using conventional counting methods [,]; namely, by counting parasites on thick films per 200 (or, in the case of very low densities, 500) leukocytes, multiplied by the patient's own leukocyte count, or if this was not available, a standard count of 8,000 leukocytes/μl. Specimens with very high counts (> 100 parasites per 100× objective field) were also counted on thin blood films as the proportion of infected erythrocytes multiplied by either the patient's red cell count, or, if this was not known, a standard red cell count (5 × 106 cells/μl).Using a 50× objective in a conventional laboratory microscope (Olympus BX 41, Olympus Australia, Oakleigh, Victoria), sequential blood film images were captured by means of a Nikon DXM1200 digital camera [] and Nikon ACT-1 software (Nikon Corporation, Tokyo, Japan) as uncompressed tagged image file format (TIFF) files at a resolution of 1,280 × 1,024 pixels. Apart from avoiding the irregular edges of the thick film and ensuring no overlapping of images, no special selection of captured fields was done. The number of leukocytes per image was recorded manually at the time of capture.ImageJ (version 1.41)[], an open-access Java-based image-processing programme, was used for image analysis. In essence, the programme segments or classifies particles to be counted on the basis of their relative density (darkness) compared with the background, via a thresholding process. Particle size (area) and degree of roundness are other classification variables. Fine morphological and differential staining characteristics of parasites are ignored. Therefore, non-parasite particles, that is, artifacts of various types, may also be segmented and are collectively termed noise (N). The target particles (malaria parasites) are the signal (S).Precise enumeration of parasites per image (the 'gold standard' for this study), was done by manually counting parasites on the captured images (in total, about 98,000 parasites in 497 images from 20 specimens were counted). A 'Point Picker' plugin [] that digitally tags each counted parasite and records its coordinates for future reference, was used to facilitate manual counting. Using the particle analysis commands of ImageJ, parasites were then counted digitally on the same images. Between 20 and 30 (mean, 25) images per slide were analysed simultaneously in a stack; the amount of virtual memory available to the image analysis software constrains stack size. Three hundred images, representing 12 different slides (calibrators), were used in the calibration experiments described below; 197 new images from eight re-sampled or duplicate slides were used to validate the findings and assess reproducibility.Statistical evaluations were done using Statistica 8.0 (StatSoft, Tulsa, OK). Because of non-normal distributions of data sets and small sample sizes (n < 30), non-parametric tests were used. Statistical evaluation at individual slide level was by signed rank tests that compared numerical results of manual and digital counting methods, together with the rank order correlation coefficient (R) as a measure of reliability of digital counts. Non-parametric ANOVA was used to compare collective counts by the three methods (conventional, manual, and digital). World Health Organization (WHO) criteria for evaluating accuracy of counts done by working microscopists, expressed as the percentage absolute discrepancy between experimental and reference counts, were used in a less stringent but practical comparison system []. […]

Pipeline specifications

Software tools ImageJ, Statistica
Applications Miscellaneous, Microscopic phenotype analysis
Organisms Homo sapiens
Diseases Hematologic Diseases, Malaria