An Image Analysis Algorithm for Malaria Parasite Stage Classification and Viability Quantification

Abstract

With more than 40% of the world’s population at risk, 200–300 million infections each year, and an estimated 1.2 million deaths annually, malaria remains one of the most important public health problems of mankind today. With the propensity of malaria parasites to rapidly develop resistance to newly developed therapies, and the recent failures of artemisinin-based drugs in Southeast Asia, there is an urgent need for new antimalarial compounds with novel mechanisms of action to be developed against multidrug resistant malaria. We present here a novel image analysis algorithm for the quantitative detection and classification of Plasmodium lifecycle stages in culture as well as discriminating between viable and dead parasites in drug-treated samples. This new algorithm reliably estimates the number of red blood cells (isolated or clustered) per fluorescence image field, and accurately identifies parasitized erythrocytes on the basis of high intensity DAPI-stained parasite nuclei spots and Mitotracker-stained mitochondrial in viable parasites. We validated the performance of the algorithm by manual counting of the infected and non-infected red blood cells in multiple image fields, and the quantitative analyses of the different parasite stages (early rings, rings, trophozoites, schizonts) at various time-point post-merozoite invasion, in tightly synchronized cultures. Additionally, the developed algorithm provided parasitological effective concentration 50 (EC50) values for both chloroquine and artemisinin, that were similar to known growth inhibitory EC50 values for these compounds as determined using conventional SYBR Green I and lactate dehydrogenase-based assays.

<strong>A portable image-based cytometer for rapid malaria detection and quantification</strong>

“…Table 2 shows the inspection results of RBCs, iRBCs segmentation and stagespecific classification. The ability of our proposed cytometer to classify and document different developmental stages may lead to potential pharmacological applications in identifying the potential blockers of egress, invasion and parasitic development inhibitory molecules [38]. In * MC represents the average value of two manual counting performed in the same image which was analysed by image-based cytometer.…”

“…Furthermore, manual counting from Giemsa-smears is known to vary depending on the personnel engaged and quality of smears involving a high chance of misinterpreting other microorganisms such as bacteria and fungi as Plasmodium parasites and difficulties associated with identifying different Plasmodium strains [31]. Previously, several laboratories explored image processing based automated cell counting for parasitemia estimation, but most of them suffered from high false positive values [32][33][34], inferior accuracy [35], inability to differentiate parasitic stages [36] primarily rings [37] and the requirement of fluorescent dyes [38,39].…”

Chemically induced strong cellular hypertrophy often reduces the accuracy of cytotoxicity measurements obtained using the ATP assay

“…MitoTracker is a mitochondria-specific fluorescent probe used to assess mitochondrial location, area, and morphological changes (Moon et al, 2013;Okuda et al, 2011). This was the probe used in this study to measure mitochondrial area.…”

Machine learning‐based in‐line holographic sensing of unstained malaria‐infected red blood cells

“…Moreover, blood smears were required after the staining procedure. Fluorescent microscopy has been employed to automatically diagnose malaria . However, exogenous labeling agents were used in the diagnosis.…”