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Registros recuperados : 14 | |
3. | ![Imagem marcado/desmarcado](/consulta/web/img/desmarcado.png) | CAMPOS, M. M.; LIMA, J. A. M.; LEÃO, J. M.; MACHADO, F. S. Eficiência bioenergética em bovinos de leite. In: VILELA, D.; FERREIRA, R. de P.; FERNANDES, E. N.; JUNTOLLI, F. V. (Ed.). Pecuária de leite no Brasil: cenários e avanços tecnológicos. Brasília, DF: Embrapa, 2016. p. 359-373 Biblioteca(s): Embrapa Gado de Leite. |
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6. | ![Imagem marcado/desmarcado](/consulta/web/img/desmarcado.png) | SOUZA, R. C. D.; REIS, R. B.; LOPES, F. C. F.; LEÃO, J. M.; MOURTHÉ, M. H. F. Productive parameters, metabolic and economic viability of dairy cows supplemented with different levels of urea in diets based on sugarcane. Journal of Animal Science, v. 92, p. 803, 2014. Supplement. Edição dos abstracts do Joint Annual Meeting, 2014, Kansas City. Biblioteca(s): Embrapa Gado de Leite. |
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8. | ![Imagem marcado/desmarcado](/consulta/web/img/desmarcado.png) | MOLINA, L. R.; COSTA, H. N.; LEÃO, J. M.; MALACCO, V. M. R.; FACURY FILHO, E. J.; CARVALHO, A. U.; LAGE, C. F. A. Efficacy of an internal teat seal associated with a dry cow intramammary antibiotic for prevention of intramammary infections in dairy cows during the dry and early lactation periods. Pesquisa Veterinária Brasileira, Rio de Janeiro, v. 37, n. 5, p. 465-470, maio 2017. Título em português: Eficácia de um selante interno de tetos associado a antibiótico de vaca seca na prevenção de infecções intramamárias no período seco e início de lactação em vacas leiteiras. Biblioteca(s): Embrapa Unidades Centrais. |
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9. | ![Imagem marcado/desmarcado](/consulta/web/img/desmarcado.png) | SOUZA, R. S. de; LEÃO, J. M.; CAMPOS, J. C.; COELHO, S. G.; CAMPOS, M. M.; LIMA, J. A. M.; FARIA, B. K. A. Avaliação da correlação entre proteína plasmática total do soro de bezerras F1 mestiças Holandês x Zebu avaliada com refratômetro óptico e digital. In: REUNIÃO ANUAL DA SOCIEDADE BRASILEIRA DE ZOOTECNIA, 52., 2015, Belo Horizonte. Zootecnia: otimizando recursos e potencialidades: anais. Belo Horizonte: Sociedade Brasileira de Zootecnia, 2015. 3 p. Biblioteca(s): Embrapa Gado de Leite. |
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10. | ![Imagem marcado/desmarcado](/consulta/web/img/desmarcado.png) | RAMOS JUNIOR, W. L.; LEAO, J. M.; CAMPOS, M. M.; COELHO, S. G.; FERREIRA, A. L.; PEREIRA, L. G. R.; TOMICH, T. R.; RIBEIRO, A. K. do C.; MACHADO, F. S. Consumo alimentar residual de bezerras F1 Holandês-Gir no período pré-desmame e sua associação com a produção de calor. In: WORKSHOP DE INICIAÇÃO CIENTÍFICA DA EMBRAPA GADO DE LEITE, 21., 2018, Juiz de Fora. Anais... Juiz de Fora: Embrapa Gado de Leite, 2018. Biblioteca(s): Embrapa Gado de Leite. |
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11. | ![Imagem marcado/desmarcado](/consulta/web/img/desmarcado.png) | LEÃO, J. M.; MACHADO, F. S.; CAMPOS, M. M.; CARNEIRO, J. C.; MARTINS, P. C.; COSTA, I. C.; SILVA, P. S. D.; FARIA, B. K. A.; LIMA, J. A. M.; SOUZA, R. S. de; COELHO, S. G. Growth performance in Crossbred (Holstein x Gyr) calves differing in phenotypic residual feed intake on pre-weaned period. In: ADSA ASAS JOINT ANNUAL MEETING, 2015, Orlando. Proceedings... Orlando: ADSA: ASAS, 2015. Biblioteca(s): Embrapa Gado de Leite. |
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12. | ![Imagem marcado/desmarcado](/consulta/web/img/desmarcado.png) | OLIVEIRA, D. A. B.; BRESOLIN, T.; COELHO, S. G.; CAMPOS, M. M.; LAGE, C. F. A.; LEÃO, J. M.; PEREIRA, L. G. R.; HERNANDEZ, L.; DOREA, J. R. R. A polar transformation augmentation approach for enhancing mammary gland segmentation in ultrasound images. Computers and Electronics in Agriculture, v. 220, 108825, 2024. Biblioteca(s): Embrapa Gado de Leite. |
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13. | ![Imagem marcado/desmarcado](/consulta/web/img/desmarcado.png) | LEÃO, J. M.; COELHO, S. G.; MACHADO, F. S.; AZEVEDO, R. A.; LIMA, J. A. M.; CARNEIRO, J. da C.; LAGE, C. F. A.; FERREIRA, A. L.; PEREIRA, L. G. R.; TOMICH, T. R.; CAMPOS, M. M. Phenotypically divergent classification of preweaned heifer calves for feed efficiency indexes and their correlations with heat production and thermography. Journal of Dairy Science, v. 101, n. 6, p. 5060-5068, 2018. Biblioteca(s): Embrapa Gado de Leite. |
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14. | ![Imagem marcado/desmarcado](/consulta/web/img/desmarcado.png) | LEÃO, J. M.; COELHO, S. G.; LAGE, C. F. de A.; AZEVEDO, R. A. de; LIMA, J. A. M.; CARNEIRO, J. C.; FERREIRA, A. L.; MACHADO, F. S.; PEREIRA, L. G. R.; TOMICH, T. R.; DINIZ NETO, H. do C.; CAMPOS, M. M. How divergence for feed efficiency traits affects body measurements and metabolites in blood and ruminal parameters on pre-weaning dairy heifers. Animals, v. 11, 3436, 2021. Biblioteca(s): Embrapa Gado de Leite. |
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Registros recuperados : 14 | |
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![](/consulta/web/img/deny.png) | Acesso ao texto completo restrito à biblioteca da Embrapa Gado de Leite. Para informações adicionais entre em contato com cnpgl.biblioteca@embrapa.br. |
Registro Completo
Biblioteca(s): |
Embrapa Gado de Leite. |
Data corrente: |
20/03/2024 |
Data da última atualização: |
20/03/2024 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
A - 1 |
Autoria: |
OLIVEIRA, D. A. B.; BRESOLIN, T.; COELHO, S. G.; CAMPOS, M. M.; LAGE, C. F. A.; LEÃO, J. M.; PEREIRA, L. G. R.; HERNANDEZ, L.; DOREA, J. R. R. |
Afiliação: |
DARIO A. B. OLIVEIRA, UNIVERSITY OF WISCONSIN-MADISON; TIAGO BRESOLIN, UNIVERSITY OF ILLINOIS; SANDRA G. COELHO, UNIVERSIDADE FEDERAL DE MINAS GERAIS; MARIANA MAGALHAES CAMPOS, CNPGL; UNIVERSIDADE FEDERAL DE MINAS GERAIS; UNIVERSIDADE FEDERAL DE MINAS GERAIS; LUIZ GUSTAVO RIBEIRO PEREIRA, CNPGL; LAURA HERNANDEZ, UNIVERSITY OF WISCONSIN-MADISON; JOÃO R. R. DOREA, UNIVERSITY OF WISCONSIN-MADISON. |
Título: |
A polar transformation augmentation approach for enhancing mammary gland segmentation in ultrasound images. |
Ano de publicação: |
2024 |
Fonte/Imprenta: |
Computers and Electronics in Agriculture, v. 220, 108825, 2024. |
DOI: |
https://doi.org/10.1016/j.compag.2024.108825 |
Idioma: |
Inglês |
Conteúdo: |
Environmental factors can detrimentally affect mammary gland development, leading to negative impacts on milk secretion in mammals. Ultrasonography serves as a non-invasive and non-destructive method for assessing mammary gland characteristics and development. Deep learning approaches enable automated monitoring of mammary gland development, though they typically require large, labeled datasets that may be limited by data collection constraints. This study aimed to develop and evaluate a polar transformation-based augmentation strategy to enhance the performance of deep learning algorithms for mammary gland segmentation in small datasets. We collected 405 ultrasound images of mammary glands (front and rear quarters) from 29 crossbred F1 Holstein x Gyr calves aged 5 to 11 weeks. The parenchyma tissue in these images was manually annotated using the VGG Image Annotator. A leave-one-animal-out cross-validation approach was employed to train the semantic segmentation algorithm. In this approach, all images from one calf were used as a testing set, and images from the remaining 28 calves were used for training in each of the 29 iterations. Our proposed method involved utilizing a polar transform technique for data augmentation in ultrasound images and the PSPNet deep learning algorithm for image segmentation. The average F1-score on the testing set was 54% in week 1, 70% in week 2, and 75% in week 3. Our findings revealed that the algorithm’s performance was suboptimal for images with very small parenchyma (week 1). However, as the mammary gland developed, the identification and segmentation of parenchymal tissue significantly improved. The performance of deep learning algorithms in segmenting small tissues could potentially be enhanced by using larger datasets and higher resolution images. In conclusion, our study demonstrates that polar transformation is an effective strategy for augmenting mammary gland ultrasound images, which in turn improves the performance of deep neural networks in segmenting parenchymal tissue. MenosEnvironmental factors can detrimentally affect mammary gland development, leading to negative impacts on milk secretion in mammals. Ultrasonography serves as a non-invasive and non-destructive method for assessing mammary gland characteristics and development. Deep learning approaches enable automated monitoring of mammary gland development, though they typically require large, labeled datasets that may be limited by data collection constraints. This study aimed to develop and evaluate a polar transformation-based augmentation strategy to enhance the performance of deep learning algorithms for mammary gland segmentation in small datasets. We collected 405 ultrasound images of mammary glands (front and rear quarters) from 29 crossbred F1 Holstein x Gyr calves aged 5 to 11 weeks. The parenchyma tissue in these images was manually annotated using the VGG Image Annotator. A leave-one-animal-out cross-validation approach was employed to train the semantic segmentation algorithm. In this approach, all images from one calf were used as a testing set, and images from the remaining 28 calves were used for training in each of the 29 iterations. Our proposed method involved utilizing a polar transform technique for data augmentation in ultrasound images and the PSPNet deep learning algorithm for image segmentation. The average F1-score on the testing set was 54% in week 1, 70% in week 2, and 75% in week 3. Our findings revealed that the algorithm’s performance was suboptimal for images... Mostrar Tudo |
Palavras-Chave: |
Polar transformation; Segmentação semântica; Semantic segmentation; Transformação polar; Ultrassonografia. |
Thesagro: |
Bovino; Glândula Mamaria; Ultrassom. |
Thesaurus NAL: |
Mammary glands; Ultrasonography. |
Categoria do assunto: |
L Ciência Animal e Produtos de Origem Animal |
Marc: |
LEADER 03118naa a2200349 a 4500 001 2163042 005 2024-03-20 008 2024 bl uuuu u00u1 u #d 024 7 $ahttps://doi.org/10.1016/j.compag.2024.108825$2DOI 100 1 $aOLIVEIRA, D. A. B. 245 $aA polar transformation augmentation approach for enhancing mammary gland segmentation in ultrasound images.$h[electronic resource] 260 $c2024 520 $aEnvironmental factors can detrimentally affect mammary gland development, leading to negative impacts on milk secretion in mammals. Ultrasonography serves as a non-invasive and non-destructive method for assessing mammary gland characteristics and development. Deep learning approaches enable automated monitoring of mammary gland development, though they typically require large, labeled datasets that may be limited by data collection constraints. This study aimed to develop and evaluate a polar transformation-based augmentation strategy to enhance the performance of deep learning algorithms for mammary gland segmentation in small datasets. We collected 405 ultrasound images of mammary glands (front and rear quarters) from 29 crossbred F1 Holstein x Gyr calves aged 5 to 11 weeks. The parenchyma tissue in these images was manually annotated using the VGG Image Annotator. A leave-one-animal-out cross-validation approach was employed to train the semantic segmentation algorithm. In this approach, all images from one calf were used as a testing set, and images from the remaining 28 calves were used for training in each of the 29 iterations. Our proposed method involved utilizing a polar transform technique for data augmentation in ultrasound images and the PSPNet deep learning algorithm for image segmentation. The average F1-score on the testing set was 54% in week 1, 70% in week 2, and 75% in week 3. Our findings revealed that the algorithm’s performance was suboptimal for images with very small parenchyma (week 1). However, as the mammary gland developed, the identification and segmentation of parenchymal tissue significantly improved. The performance of deep learning algorithms in segmenting small tissues could potentially be enhanced by using larger datasets and higher resolution images. In conclusion, our study demonstrates that polar transformation is an effective strategy for augmenting mammary gland ultrasound images, which in turn improves the performance of deep neural networks in segmenting parenchymal tissue. 650 $aMammary glands 650 $aUltrasonography 650 $aBovino 650 $aGlândula Mamaria 650 $aUltrassom 653 $aPolar transformation 653 $aSegmentação semântica 653 $aSemantic segmentation 653 $aTransformação polar 653 $aUltrassonografia 700 1 $aBRESOLIN, T. 700 1 $aCOELHO, S. G. 700 1 $aCAMPOS, M. M. 700 1 $aLAGE, C. F. A. 700 1 $aLEÃO, J. M. 700 1 $aPEREIRA, L. G. R. 700 1 $aHERNANDEZ, L. 700 1 $aDOREA, J. R. R. 773 $tComputers and Electronics in Agriculture$gv. 220, 108825, 2024.
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