Algoritmo para Classificação de Eritrócitos Micronucleados em Imagens Microscópicas de Sangue Periférico
Resumo
O Teste do Micronúcleo in vivo é um ensaio eficiente e de baixo custo utilizado na detecção de mutagenicidade induzida por substâncias, sua análise microscópica consiste na contagem e classificação de eritrócitos entre normais e micronucleados, sendo demorada e laboriosa. Nesse trabalho, propõe-se um algoritmo para classificação automática dos eritrócitos utilizando imagens microscópicas e aprendizado de máquina supervisionado. A Rede Neural Convolucional usada segue o modelo LeNet, tem entrada de 120x120 e foi treinada utilizando 1600 imagens. O classificador obteve sensibilidade de 95,25%, especificidade 98,50% e acurácia de 96,87%.
Referências
Bahreyni Toossi, M. T., Azimian, H., Sarrafzadeh, O., Mohebbi, S., and Soleymanifard, S. (2017). Automatic detection of micronuclei by cell microscopic image processing. Mutation Research/Fundamental and Molecular Mechanisms of Mutagenesis, 806: 9–18.
Ceppi, M., Biasotti, B., Fenech, M., and Bonassi, S. (2010). Human population studies with the exfoliated buccal micronucleus assay: Statistical and epidemiological issues. Mutation Research/Reviews in Mutation Research, 705(1): 11–19.
Custer, L., Doherty, A., and Proudlock, R. (2016). The In Vivo Rodent Micronucleus Assay. Genetic Toxicology Testing: A Laboratory Manual. Elsevier Science Publishing Company, 269-322
Frenzilli, G., Nigro, M., and Lyons, B. (2009). The Comet assay for the evaluation of genotoxic impact in aquatic environments. Mutation Research/Reviews in Mutation Research, 681(1): 80–92
Frieauff, W., Martus, H. J., Suter, W. and Elhajouji, A. (2013). Automatic analysis of the micronucleus test in primary human lymphocytes using image analysis. Mutagenesis, 28(1): 15–23
Han, D., Liu, Q. and Fan, W. (2018). A new image classification method using CNN transfer learning and web data augmentation. Expert Systems with Applications, 95: 43–56.
Iano, Y., Arthur, R., Saotome, O., Vieira Estrela, V., and Loschi, H. J. (2019). Proceedings of the 4th Brazilian Technology Symposium (BTSym’18). Smart Innovation, Systems and Technologies.
Lebaron, M. J., Schisler, M. R., Torous, D. K., Dertinger, S. D. and Gollapudi, B. B. (2013). Influence of counting methodology on erythrocyte ratios in the mouse micronucleus test. Environmental and Molecular Mutagenesis, 54(3): 222–228.
LeCun Y., Bottou, L., Bengio, Y and Haffner, P. (1988). Gradient-Based Learning Applied to Document Recognition. Proc. of the IEEE.
Li, Q., Cai, W., Wang, X., Zhou, Y., Feng, D. D., and Chen, M. (2014). Medical image classification with convolutional neural network. 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV).
Macêdo, L., Pimentel, M., Araújo dos Santos, F. and Cunha Dias, R. (2015). A eritropoiese e o eritrograma: Uma Revisão. Revista Brasileira de Higiene e Sanidade Animal: RBHSA, 9(4): 716–732.
Oecd. (2010). In Vitro Mammalian Cell Micronucleus Test. OECD Guidelines for the Testing of Chemicals.
Pereira, L. C., de Souza, A. O., Bernardes, M. F. F., Pazin, M., Tasso, M. J., Pereira, P. H., and Dorta, D. J. (2015). A perspective on the potential risks of emerging contaminants to human and environmental health. Environmental Science and Pollution Research, 22(18): 13800–13823.
Rodrigues, M. A. (2018). Automation of the in vitro micronucleus assay using the Imagestream® imaging flow cytometer. Cytometry Part A, 93(7): 706–726.
Salamon, J. and Bello, J. P. (2017). Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification. IEEE Signal Processing Letters, 24(3): 279–283.
Savkare, S. S., and Narote, S. P. (2012). Automatic System for Classification of Erythrocytes Infected with Malaria and Identification of Parasite’s Life Stage. Procedia Technology, 6: 405–410.
Shibai-Ogata, A., Tahara, H., Yamamoto, Y., Fujita, M., Satoh, H., Yuasa, A. and Kasahara, T. (2014). An automated new technique for scoring the in vivo micronucleus assay with image analysis. Mutagenesis, 29(1): 63–71.
Ceppi, M., Biasotti, B., Fenech, M., and Bonassi, S. (2010). Human population studies with the exfoliated buccal micronucleus assay: Statistical and epidemiological issues. Mutation Research/Reviews in Mutation Research, 705(1): 11–19.
Custer, L., Doherty, A., and Proudlock, R. (2016). The In Vivo Rodent Micronucleus Assay. Genetic Toxicology Testing: A Laboratory Manual. Elsevier Science Publishing Company, 269-322
Frenzilli, G., Nigro, M., and Lyons, B. (2009). The Comet assay for the evaluation of genotoxic impact in aquatic environments. Mutation Research/Reviews in Mutation Research, 681(1): 80–92
Frieauff, W., Martus, H. J., Suter, W. and Elhajouji, A. (2013). Automatic analysis of the micronucleus test in primary human lymphocytes using image analysis. Mutagenesis, 28(1): 15–23
Han, D., Liu, Q. and Fan, W. (2018). A new image classification method using CNN transfer learning and web data augmentation. Expert Systems with Applications, 95: 43–56.
Iano, Y., Arthur, R., Saotome, O., Vieira Estrela, V., and Loschi, H. J. (2019). Proceedings of the 4th Brazilian Technology Symposium (BTSym’18). Smart Innovation, Systems and Technologies.
Lebaron, M. J., Schisler, M. R., Torous, D. K., Dertinger, S. D. and Gollapudi, B. B. (2013). Influence of counting methodology on erythrocyte ratios in the mouse micronucleus test. Environmental and Molecular Mutagenesis, 54(3): 222–228.
LeCun Y., Bottou, L., Bengio, Y and Haffner, P. (1988). Gradient-Based Learning Applied to Document Recognition. Proc. of the IEEE.
Li, Q., Cai, W., Wang, X., Zhou, Y., Feng, D. D., and Chen, M. (2014). Medical image classification with convolutional neural network. 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV).
Macêdo, L., Pimentel, M., Araújo dos Santos, F. and Cunha Dias, R. (2015). A eritropoiese e o eritrograma: Uma Revisão. Revista Brasileira de Higiene e Sanidade Animal: RBHSA, 9(4): 716–732.
Oecd. (2010). In Vitro Mammalian Cell Micronucleus Test. OECD Guidelines for the Testing of Chemicals.
Pereira, L. C., de Souza, A. O., Bernardes, M. F. F., Pazin, M., Tasso, M. J., Pereira, P. H., and Dorta, D. J. (2015). A perspective on the potential risks of emerging contaminants to human and environmental health. Environmental Science and Pollution Research, 22(18): 13800–13823.
Rodrigues, M. A. (2018). Automation of the in vitro micronucleus assay using the Imagestream® imaging flow cytometer. Cytometry Part A, 93(7): 706–726.
Salamon, J. and Bello, J. P. (2017). Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification. IEEE Signal Processing Letters, 24(3): 279–283.
Savkare, S. S., and Narote, S. P. (2012). Automatic System for Classification of Erythrocytes Infected with Malaria and Identification of Parasite’s Life Stage. Procedia Technology, 6: 405–410.
Shibai-Ogata, A., Tahara, H., Yamamoto, Y., Fujita, M., Satoh, H., Yuasa, A. and Kasahara, T. (2014). An automated new technique for scoring the in vivo micronucleus assay with image analysis. Mutagenesis, 29(1): 63–71.
Publicado
26/12/2019
Como Citar
DA SILVA, Judson B.; SOUSA, Aldir Silva.
Algoritmo para Classificação de Eritrócitos Micronucleados em Imagens Microscópicas de Sangue Periférico. In: ESCOLA REGIONAL DE COMPUTAÇÃO APLICADA À SAÚDE (ERCAS), 7. , 2019, Teresina.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 217-222.
