Segmentação de imagens de ultrassonografia da carcaça em pequenos ruminantes utilizando Deep Learning
Resumo
Brazil is one of the main countries operating in the agribusiness sector. Sheep and goat farming is one of the segments of Brazilian agribusiness. The evaluation of each carcass of goats and sheep is carried out by a specialist who evaluates them based on visual aspects, being susceptible to errors in the final evaluation. In this context, the purpose of this work is to use Convolutional Neural Networks to segment the Longissimus dorsi muscle area in ultrasonographic images of small ruminants. Our experiments showed that the PSPNet CNN architecture achieved the results with an Intersect over Union (IoU) rate of 0.89. It was possible to obtain a precise segmentation of the images, which will allow the producer to correctly diagnose the measurements of the animals with greater practicality and saving time.
Referências
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Chaurasia, A. and Culurciello, E. (2017). Linknet: Exploiting encoder representations for efficient semantic segmentation. In 2017 IEEE Visual Communications and Image Processing (VCIP), pages 1–4. IEEE.
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Daniel, H., González, G. V., García, M. V., Rivero, A. J. L., and De Paz, J. F. (2020). Non-invasive automatic beef carcass classification based on sensor network and image analysis. Future Generation Computer Systems, 113:318–328.
Font-i Furnols, M., Brun, A., Marti, S., Realini, C., Pérez-Juan, M., Gonzalez, J., and Devant, M. (2014). Composition and intramuscular fat estimation of holstein bull and steer rib sections by using one or more computed tomography cross-sectional images. Livestock Science, 170:210–218.
IBGE (2020). Pesquisa da pecuária municipal. Último acesso em 13 de outubro de 2020.
Jain, A. K. and Li, S. Z. (2011). Handbook of face recognition, volume 1. Springer.
Kempster, A., Cuthbertson, A., Harrington, G., et al. (1982). Carcase evaluation in livestock breeding, production and marketing. Granada Publishing Limited.
Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017). Feature In Proceedings of the IEEE conference on pyramid networks for object detection. computer vision and pattern recognition, pages 2117–2125.
Lindner, C., Wang, C.-W., Huang, C.-T., Li, C.-H., Chang, S.-W., and Cootes, T. F. (2016). Fully automatic system for accurate localisation and analysis of cephalometric landmarks in lateral cephalograms. Scientic reports, 6:33581.
Neto, A. D. B. (2010). Posicionamento estratégico do setor de carnes de caprinos e ovinos no mercado de carnes brasileiro. Revista Tecnologia & Ciência Agropecuária, 4(4):81–85.
Nowozin, S. (2014). Optimal decisions from probabilistic models: the intersection-overunion case. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 548–555.
Oliver, A., Mendizabal, J., Ripoll, G., Albertí, P., and Purroy, A. (2010). Predicting meat yields and commercial meat cuts from carcasses of young bulls of spanish breeds by the seurop method and an image analysis system. Meat science, 84(4):628–633.
Peña, F., Santos, R., Juárez, M., Avilés, C., Domenech, V., González, A., Martínez, A., and Molina, A. (2014). The use of ultrasound scanning at different times of the nishing period in lean cattle. Livestock Science, 167:381–391.
Ronneberger, O., Fischer, P., and Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention, pages 234–241. Springer.
ROSANOVA, C. (2004). Fatores favoráveis e limitantes ao desenvolvimento da cadeia produtiva da ovinocaprinocultura de corte no brasil. Monografia, Universidade Federal de Lavras.
Tauroco (2020). A utilização da técnica de ultrassonografia em tempo real para avaliação e seleção de características de carcaça em animais de corte.
Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (2017). Pyramid scene parsing network. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2881–2890.
Cartaxo, F. Q. and Sousa, W. H. d. (2008). Correlações entre as características obtidas in vivo por ultra-som e as obtidas na carcaça de cordeiros terminados em confinamento. Revista Brasileira de Zootecnia, 37:1490–1495.
Chaurasia, A. and Culurciello, E. (2017). Linknet: Exploiting encoder representations for efficient semantic segmentation. In 2017 IEEE Visual Communications and Image Processing (VCIP), pages 1–4. IEEE.
Cutler, A., Cutler, D. R., and Stevens, J. R. (2012). Random forests. In Ensemble machine learning, pages 157–175. Springer.
Daniel, H., González, G. V., García, M. V., Rivero, A. J. L., and De Paz, J. F. (2020). Non-invasive automatic beef carcass classification based on sensor network and image analysis. Future Generation Computer Systems, 113:318–328.
Font-i Furnols, M., Brun, A., Marti, S., Realini, C., Pérez-Juan, M., Gonzalez, J., and Devant, M. (2014). Composition and intramuscular fat estimation of holstein bull and steer rib sections by using one or more computed tomography cross-sectional images. Livestock Science, 170:210–218.
IBGE (2020). Pesquisa da pecuária municipal. Último acesso em 13 de outubro de 2020.
Jain, A. K. and Li, S. Z. (2011). Handbook of face recognition, volume 1. Springer.
Kempster, A., Cuthbertson, A., Harrington, G., et al. (1982). Carcase evaluation in livestock breeding, production and marketing. Granada Publishing Limited.
Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017). Feature In Proceedings of the IEEE conference on pyramid networks for object detection. computer vision and pattern recognition, pages 2117–2125.
Lindner, C., Wang, C.-W., Huang, C.-T., Li, C.-H., Chang, S.-W., and Cootes, T. F. (2016). Fully automatic system for accurate localisation and analysis of cephalometric landmarks in lateral cephalograms. Scientic reports, 6:33581.
Neto, A. D. B. (2010). Posicionamento estratégico do setor de carnes de caprinos e ovinos no mercado de carnes brasileiro. Revista Tecnologia & Ciência Agropecuária, 4(4):81–85.
Nowozin, S. (2014). Optimal decisions from probabilistic models: the intersection-overunion case. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 548–555.
Oliver, A., Mendizabal, J., Ripoll, G., Albertí, P., and Purroy, A. (2010). Predicting meat yields and commercial meat cuts from carcasses of young bulls of spanish breeds by the seurop method and an image analysis system. Meat science, 84(4):628–633.
Peña, F., Santos, R., Juárez, M., Avilés, C., Domenech, V., González, A., Martínez, A., and Molina, A. (2014). The use of ultrasound scanning at different times of the nishing period in lean cattle. Livestock Science, 167:381–391.
Ronneberger, O., Fischer, P., and Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention, pages 234–241. Springer.
ROSANOVA, C. (2004). Fatores favoráveis e limitantes ao desenvolvimento da cadeia produtiva da ovinocaprinocultura de corte no brasil. Monografia, Universidade Federal de Lavras.
Tauroco (2020). A utilização da técnica de ultrassonografia em tempo real para avaliação e seleção de características de carcaça em animais de corte.
Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (2017). Pyramid scene parsing network. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2881–2890.
Publicado
23/11/2021
Como Citar
OLIVEIRA, Tiago M.; SARMENTO, José L. R.; FIGUEIREDO FILHO, Luiz A. S.; SILVA, Romuere R. V..
Segmentação de imagens de ultrassonografia da carcaça em pequenos ruminantes utilizando Deep Learning. In: ENCONTRO UNIFICADO DE COMPUTAÇÃO DO PIAUÍ (ENUCOMPI), 14. , 2021, Picos.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 144-151.
DOI: https://doi.org/10.5753/enucompi.2021.17765.
