Assessment of ICount software, a precise and fast egg counting tool for the mosquito vector Aedes aegypti

Gaburro, Julie; Duchemin, Jean‐Bernard; Paradkar, Prasad N.; Nahavandi, Saeid; Bhatti, Asim

doi:10.1186/s13071-016-1870-1

Cited by 25 publications

(23 citation statements)

References 21 publications

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“…Within the field of image feature analysis, studies have been conducted to identify mosquitoes according to their life cycle stage (egg, larval phase, pupal stage and adult). Some studies have attempted to automatize the process of egg counting to assess the level of fecundity and thereby estimate the mosquito population size and conduct morphometric analysis[35–38]. Studies conducted to classify mosquito-based image in adult phase still focus on parts of the body of the mosquito.…”

Section: Introductionmentioning

confidence: 99%

Application of convolutional neural networks for classification of adult mosquitoes in the field

et al. 2019

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Dengue, chikungunya and Zika are arboviruses transmitted by mosquitos of the genus Aedes and have caused several outbreaks in world over the past ten years. Morphological identification of mosquitos is currently restricted due to the small number of adequately trained professionals. We implemented a computational model based on a convolutional neural network (CNN) to extract features from mosquito images to identify adult mosquitoes from the species Aedes aegypti, Aedes albopictus and Culex quinquefasciatus. To train the CNN to perform automatic morphological classification of mosquitoes, we used a dataset that included 4,056 mosquito images. Three neural networks, including LeNet, AlexNet and GoogleNet, were used. During the validation phase, the accuracy of the mosquito classification was 57.5% using LeNet, 74.7% using AlexNet and 83.9% using GoogleNet. During the testing phase, the best result (76.2%) was obtained using GoogleNet; results of 52.4% and 51.2% were obtained using LeNet and AlexNet, respectively. Significantly, accuracies of 100% and 90% were achieved for the classification of Aedes and Culex, respectively. A classification accuracy of 82% was achieved for Aedes females. Our results provide information that is fundamental for the automatic morphological classification of adult mosquito species in field. The use of CNN's is an important method for autonomous identification and is a valuable and accessible resource for health workers and taxonomists for the identification of some insects that can transmit infectious agents to humans.

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Section: Introductionmentioning

confidence: 99%

Application of convolutional neural networks for classification of adult mosquitoes in the field

et al. 2019

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“…The algorithm also gives the two dimensions (2D) location of the females when they move during the recording to visualize space occupation linked to their activity. Finally, the eggs were collected after the end of each oviposition period and counted with Icount tool 54 .…”

Section: Methodsmentioning

confidence: 99%

Neurotropism and behavioral changes associated with Zika infection in the vector Aedes aegypti

Gaburro

Bhatti

Harper

et al. 2018

Emerging Microbes & Infections

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Understanding Zika virus infection dynamics is essential, as its recent emergence revealed possible devastating neuropathologies in humans, thus causing a major threat to public health worldwide. Recent research allowed breakthrough in our understanding of the virus and host pathogenesis; however, little is known on its impact on its main vector, Aedes aegypti. Here we show how Zika virus targets Aedes aegypti’s neurons and induces changes in its behavior. Results are compared to dengue virus, another flavivirus, which triggers a different pattern of behavioral changes. We used microelectrode array technology to record electrical spiking activity of mosquito primary neurons post infections and discovered that only Zika virus causes an increase in spiking activity of the neuronal network. Confocal microscopy also revealed an increase in synapse connections for Zika virus-infected neuronal networks. Interestingly, the results also showed that mosquito responds to infection by overexpressing glutamate regulatory genes while maintaining virus levels. This neuro-excitation, possibly via glutamate, could contribute to the observed behavioral changes in Zika virus-infected Aedes aegypti females. This study reveals the importance of virus-vector interaction in arbovirus neurotropism, in humans and vector. However, it appears that the consequences differ in the two hosts, with neuropathology in human host, while behavioral changes in the mosquito vector that may be advantageous to the virus.

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“…O software ICount, desenvolvido por Gaburro et al (2016), consiste de uma ferramenta computacional para a contagem semiautomática e automática dos ovos do Aedes. Durante o processo é realiza uma operação de segmentação com o objetivo de identificar o que é fundo e o que são ovos.…”

Section: Técnicas Computacionais De Contagemunclassified

Uma revisão das técnicas computacionais para contagem de ovos de Aedes aegypti em imagens de ovitrampas

Brun

Moraes

Rizzi

et al. 2020

RBCA

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A dengue é uma doença viral infecciosa, transmitida pelo mosquito Aedes (albopictus e aegypti), que está presente em mais de 100 países. Mesmo tendo um número frequente e crescente de casos confirmados, não existe vacina eficaz contra a dengue. A estratégia mais eficiente é o acompanhamento e controle do vetor, realizado principalmente com a limpeza de recipientes que são possíveis criadouros. Uma das alternativas para o monitoramento, é a estimação da quantidade de mosquitos por região. Isso pode ser feito através da contagem de ovos em imagens de ovitrampas, que são armadilhas compostas por um recipiente parcialmente ocupado por água ou alguma infusão no qual é inserida uma palheta ou lâmina, proporcionando um ambiente adequado para oviposição. A palheta é fotografada e a contagem dos ovos é feita manualmente. As imagens contêm uma quantidade variável de ovos podendo alcançar milhares de unidades, tornando o trabalho de contagem dispendioso e passivo a erros devido ao cansaço humano. Uma alternativa para auxiliar na estimação do número de ovos presentes nas ovitrampas são os métodos de contagem automática. O atual trabalho apresenta um levantamento bibliográfico de trabalhos de contagem automática de ovos presentes na literatura, visando identificar as diferentes técnicas usadas na solução do problema, bem como os fatores de maior influência no processo e possíveis desafios que ainda demandam soluções.

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Assessment of ICount software, a precise and fast egg counting tool for the mosquito vector Aedes aegypti

Cited by 25 publications

References 21 publications

Application of convolutional neural networks for classification of adult mosquitoes in the field

Application of convolutional neural networks for classification of adult mosquitoes in the field

Neurotropism and behavioral changes associated with Zika infection in the vector Aedes aegypti

Uma revisão das técnicas computacionais para contagem de ovos de Aedes aegypti em imagens de ovitrampas

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