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Registro Completo |
Biblioteca(s): |
Embrapa Milho e Sorgo. |
Data corrente: |
11/09/2012 |
Data da última atualização: |
13/09/2012 |
Tipo da produção científica: |
Artigo em Anais de Congresso |
Autoria: |
MATRANGOLO, W. J. R.; LANDAU, E. C.; ALBERNAZ, W. M.; SILVA, I. H. S. da; MIRANDA, G. A.; TRAVASSOS, L. E. P. |
Afiliação: |
WALTER JOSE RODRIGUES MATRANGOLO, CNPMS; ELENA CHARLOTTE LANDAU, CNPMS; WALFRIDO MACHADO ALBERNAZ, EMATER, Sete Lagoas, MG.; IGOR HENRIQUE SENA DA SILVA, UFSJ; GABRIEL AVELAR MIRANDA, Estagiário; LUIZ EDUARDO PANISSET TRAVASSOS, PUC Minas. |
Título: |
Diagnóstico regionalizado de sistemas de produção de milho como subsídio para gestão territorial. |
Ano de publicação: |
2012 |
Fonte/Imprenta: |
In: CONGRESSO NACIONAL DE MILHO E SORGO, 29., 2012, Águas de Lindóia. Diversidade e inovações na era dos transgênicos: resumos expandidos. Campinas: Instituto Agronômico; Sete Lagoas: Associação Brasileira de Milho e Sorgo, 2012. |
Páginas: |
p. 3584-3590. |
Descrição Física: |
1 CD-ROM. |
Idioma: |
Português |
Thesagro: |
Agricultura Familiar; Milho. |
Categoria do assunto: |
-- |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/66053/1/Diagnostico-regionalizado.pdf
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Marc: |
LEADER 00818nam a2200193 a 4500 001 1933396 005 2012-09-13 008 2012 bl uuuu u00u1 u #d 100 1 $aMATRANGOLO, W. J. R. 245 $aDiagnóstico regionalizado de sistemas de produção de milho como subsídio para gestão territorial.$h[electronic resource] 260 $aIn: CONGRESSO NACIONAL DE MILHO E SORGO, 29., 2012, Águas de Lindóia. Diversidade e inovações na era dos transgênicos: resumos expandidos. Campinas: Instituto Agronômico; Sete Lagoas: Associação Brasileira de Milho e Sorgo$c2012 300 $ap. 3584-3590.$c1 CD-ROM. 650 $aAgricultura Familiar 650 $aMilho 700 1 $aLANDAU, E. C. 700 1 $aALBERNAZ, W. M. 700 1 $aSILVA, I. H. S. da 700 1 $aMIRANDA, G. A. 700 1 $aTRAVASSOS, L. E. P.
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Embrapa Milho e Sorgo (CNPMS) |
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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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