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 | Acesso ao texto completo restrito à biblioteca da Embrapa Gado de Leite. Para informações adicionais entre em contato com rosangela.lacerda@embrapa.br. |
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Biblioteca(s): |
Embrapa Gado de Leite. |
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Data corrente: |
20/03/2024 |
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Data da última atualização: |
20/03/2024 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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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. |
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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. |
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Título: |
A polar transformation augmentation approach for enhancing mammary gland segmentation in ultrasound images. |
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Ano de publicação: |
2024 |
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Fonte/Imprenta: |
Computers and Electronics in Agriculture, v. 220, 108825, 2024. |
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DOI: |
https://doi.org/10.1016/j.compag.2024.108825 |
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Idioma: |
Inglês |
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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 |
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Palavras-Chave: |
Polar transformation; Segmentação semântica; Semantic segmentation; Transformação polar; Ultrassonografia. |
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Thesagro: |
Bovino; Glândula Mamaria; Ultrassom. |
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Thesaurus Nal: |
Mammary glands; Ultrasonography. |
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Categoria do assunto: |
L Ciência Animal e Produtos de Origem Animal |
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Marc: |
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Registro original: |
Embrapa Gado de Leite (CNPGL) |
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| Registros recuperados : 4 | |
| 1. |  | SILVA, R. de O.; BARIONI, L. G.; ALBERTINI, T. Z.; EORY, V.; TOPP, C. F. E.; FERNANDES, F. A.; MORAN, D. Developing a nationally appropriate mitigation measure from the greenhouse gas abatement potential from livestock production in the Brazilian Cerrado. In: LIVESTOCK, CLIMATE CHANGE AND FOOD SECURITY CONFERENCE, 2014, Madrid. Conference abstract book... [Paris]: INRA, 2014. p. 77.| Tipo: Resumo em Anais de Congresso |
| Biblioteca(s): Embrapa Agricultura Digital. |
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| 2. | | SILVA, R. de O.; BARIONI, L. G.; ALBERTINI, T. Z.; EORY, V.; TOPP, C. F. E.; FERNANDES, F. A.; MORAN, D. Developing a nationally appropriate mitigation measure from the greenhouse gas GHG abatement potential from livestock production in the Brazilian Cerrado. Agricultural Systems, v. 140, p. 48-55, Nov. 2015.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
| Biblioteca(s): Embrapa Agricultura Digital; Embrapa Pantanal. |
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| 3. | | SILVA, R. de O.; BARIONI, L. G.; ALBERTINI, T. Z.; EORY, V.; TOPP, C. F. E.; FERNANDES, F. A.; MORAN, D. Developing a nationally appropriate mitigation measure from the greenhouse gas GHG abatement potential from livestock production in the Brazilian Cerrado. In: EORY, V.; MACLEOD, M.; FAVERDIN, P.; O´BRIEN, D.; SILVA, R. de O.; BARIONI, L.G.; ALBERTINI, Z.; TOPP, K.; FERNANDES, F. A.; MORAN, D.; HUTCHINGS, N.; STIENEZEN, M.; SHALOO, L.; REES, R. M.; MOGENSEN, L.; LUND, P.; BRASK, M.; DOREAU, M.; GARCIA-LAUNAY, F.; DOURMAD, J. Y.; BENDAHAN, A. B.; VELOSO, R. F.; GONZALEZ R. D. Report on developing bottom-up Marginal Abatement Cost Curves (MACCS) for representative farm types. [S. l.: s. n.], [2015?]. p. 65-81.| Biblioteca(s): Embrapa Pantanal. |
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| 4. | | EORY, V.; MACLEOD, M.; FAVERDIN, P.; O´BRIEN, D.; SILVA, R. de O.; BARIONI, L. G.; ALBERTINI, Z.; TOPP, K.; FERNANDES, F. A.; MORAN, D.; HUTCHINGS, N.; STIENEZEN, M.; SHALOO, L.; REES, R. M.; MOGENSEN, L.; LUND, P.; BRASK, M.; DOREAU, M.; GARCIA-LAUNAY, F.; DOURMAD, J. Y.; BENDAHAN, A. B.; VELOSO, R. F.; GONZALEZ, R. D. S. Report on developing bottom-up Marginal Abatement Cost Curves (MACCS) for representative farm types. [S. l.: s. n.], 2015. 129 p.| Tipo: Autoria/Organização/Edição de Livros |
| Biblioteca(s): Embrapa Roraima. |
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| Registros recuperados : 4 | |
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