Image analysis for Eimeria oocyst counts and classification

Adams, Daniel; Ruíz-Jiménez, Fernando; Fletcher, O. J.; Gall, Sezny; Crespo, Rocio

doi:10.1016/j.japr.2022.100260

Cited by 5 publications

(4 citation statements)

References 14 publications

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“…To date, there are a few automated protocols for oocyst enumeration and speciation using flow cytometry [9] and digital image analysis [10]. However, we found that the protocols used for sample preparation for these applications are complex, and the oocyst counts may be overestimated.…”

Section: Discussionmentioning

confidence: 90%

“…Automatic and semi-automatic protocols, including molecular tests, cytometry, and digital image analysis, for the enumeration and identification of coccidia are available [7][8][9][10]. Most PCR reactions target the intergenic transcribed spacer region 1 (ITS1) of the ribosomal RNA (rRNA) gene operon [7], a multi-copy gene, which makes these protocols unsuitable for estimation of the relative abundance of species in mixed infections of Eimeria sp.…”

Section: Introductionmentioning

confidence: 99%

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Automated Image Analysis for Detection of Coccidia in Poultry

Kellogg,

Roberts,

Crespo

2024

Animals

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Coccidiosis, caused by the protozoan Eimeria sp., is one of the most common and costly diseases impacting the poultry industry. To establish effective control measures, it is essential to identify these protozoa. Typical methods for identifying and determining the severity of the protozoal infection include intestinal lesion scoring or enumeration of the protozoal oocysts in fecal samples. Standard analysis methods require highly skilled technicians or veterinarians to manually identify and manually enumerate these protozoal parasites. This process is labor intensive, time-consuming, and susceptible to human error. None of the current methods available, including molecular flow cytometry or even digital image analysis, can determine if an oocyst is sporulated or not. Oocysts are not infectious until they sporulate. The goal of this study was to design an automated model using Artificial Intelligence (AI) to expedite the process of enumeration, improve the efficiency and accuracy of the species identification, and determine the ability of the oocysts to infect. To this end, we trained and evaluated computer vision models based on the Mask RCNN neural network architecture. A model was trained to detect and differentiate three species and to determine sporulation for each (totaling six detection groups). This model achieved a mean relative percentage difference (RPD) of 5.64%, representing a slight overcount compared to manual counts, averaging across all groups. The mean RPD for each group individually fell within a range from −33.37% to 52.72%. These results demonstrate that these models were speedy and had high agreement with manual counts, with minimal processing of field-quality samples. These models also could differentiate the sporulation status of the oocysts, providing critical diagnostic information for potential field applications.

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Section: Discussionmentioning

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Automated Image Analysis for Detection of Coccidia in Poultry

Kellogg,

Roberts,

Crespo

2024

Animals

Self Cite

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“…Image J played a significant role in biological and medical image analysis [26][27][28], which had qualitative representation, co-localization of 3D images, length measurement, and detection of fluorescence intensity changes in a rectangular range. Therefore, this study used it to identify the trajectories of aerosols and droplets.…”

Section: Aerosol and Droplets Flow Track Identificationmentioning

confidence: 99%

Study of aerosol dispersion and control in dental practice

He,

Li,

Chen

et al. 2024

Clin Oral Invest

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Objectives In this study, we investigated the dispersion patterns of aerosols and droplets during oral practice, simulated the adsorption characteristics of suction devices, and evaluated the effectiveness of suction devices in reducing aerosols during dental procedures. Materials and MethodsFirstly, the continuous images of oral aerosols and droplets were photographed with a high-speed camera, and the trajectories of these particles were recognized and processed by Image J to determine key parameters affecting particle dispersion: diffusion velocity, distance, and angle.Secondly, based on the parameters data, the flow field of aerosol particles around the oral cavity was simulated using computational fluid dynamics (CFD), and the flow field under adsorption conditions was simulated to demonstrate the aerodynamic characteristics and adsorption efficiencies of the singlechannel and three-channel adsorption ports at different pressures. Finally, according to the simulated data, a three-channel suction device was developed, and the adsorption efficiency of the device was tested by the fluorescein tracer method. ResultsThe dispersion experimental data showed that aerosol particles' maximum diffusion velocity, distance, and angle were 6.2 m/s, 0.55 m, and 130°, respectively. The simulated aerosol flow-field distribution was consistent with the aerosol dispersion patterns. The adsorption simulation results showed that the adsorption efficiency of the single-channel adsorption port could reach 74.05% at -350 pa, and the three-channel port was 99.23% at -350 pa and -150 pa. The adsorption experimental data showed that the adsorption efficiency of three-channel suction device was 97.71%.Conclusions A three-channel suction device was designed by simulations and experiments, which can capture most aerosols in the dental clinic and prevent them from spreading.Clinical relevance Using three-channel suction devices during oral treatment effectively reduces the spread of oral aerosols, which is essential to prevent the spread of epidemics and ensure the health and safety of patients and dental staff.

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“…The intensity of infection with intestinal parasites is classically estimated by counting parasite (transmission) stages present in the faeces. Although automation has progressed [ 10 , 11 ], classical “coprological methods” are laborious, making it difficult to quantify parasites in high numbers of samples. They also require high technical and taxonomic expertise to distinguish between closely related species.…”

Section: Introductionmentioning

confidence: 99%

Amplicon sequencing allows differential quantification of closely related parasite species: an example from rodent Coccidia (Eimeria)

2023

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Background Quantifying infection intensity is a common goal in parasitological studies. We have previously shown that the amount of parasite DNA in faecal samples can be a biologically meaningful measure of infection intensity, even if it does not agree well with complementary counts of transmission stages (oocysts in the case of Coccidia). Parasite DNA can be quantified at relatively high throughput using quantitative polymerase chain reaction (qPCR), but amplification needs a high specificity and does not simultaneously distinguish between parasite species. Counting of amplified sequence variants (ASVs) from high-throughput marker gene sequencing using a relatively universal primer pair has the potential to distinguish between closely related co-infecting taxa and to uncover the community diversity, thus being both more specific and more open-ended. Methods We here compare qPCR to the sequencing-based amplification using standard PCR and a microfluidics-based PCR to quantify the unicellular parasite Eimeria in experimentally infected mice. We use multiple amplicons to differentially quantify Eimeria spp. in a natural house mouse population. Results We show that sequencing-based quantification has high accuracy. Using a combination of phylogenetic analysis and the co-occurrence network, we distinguish three Eimeria species in naturally infected mice based on multiple marker regions and genes. We investigate geographical and host-related effects on Eimeria spp. community composition and find, as expected, prevalence to be largely explained by sampling locality (farm). Controlling for this effect, the novel approach allowed us to find body condition of mice to be negatively associated with Eimeria spp. abundance. Conclusions We conclude that amplicon sequencing provides the underused potential for species distinction and simultaneous quantification of parasites in faecal material. The method allowed us to detect a negative effect of Eimeria infection on the body condition of mice in the natural environment. Graphical Abstract

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Image analysis for Eimeria oocyst counts and classification

Cited by 5 publications

References 14 publications

Automated Image Analysis for Detection of Coccidia in Poultry

Automated Image Analysis for Detection of Coccidia in Poultry

Study of aerosol dispersion and control in dental practice

Amplicon sequencing allows differential quantification of closely related parasite species: an example from rodent Coccidia (Eimeria)

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