FINE: improving time and precision of segmentation techniques for vertebral compression fractures in MRI (2020)
- Authors:
- USP affiliated authors: BARBOSA, MARCELLO HENRIQUE NOGUEIRA - FMRP ; TRAINA, AGMA JUCI MACHADO - ICMC ; RAMOS, JONATHAN DA SILVA - ICMC ; CAZZOLATO, MIRELA TEIXEIRA - ICMC
- Unidades: FMRP; ICMC
- DOI: 10.1145/3341105.3374100
- Subjects: INFORMÁTICA MÉDICA; METODOLOGIA E TÉCNICAS DE COMPUTAÇÃO; RESSONÂNCIA MAGNÉTICA; PROCESSAMENTO DE IMAGENS
- Keywords: vertebral compression fractures; image segmentation; automatic inside annotation
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título: Proceedings
- Conference titles: ACM/SIGAPP Symposium on Applied Computing - SAC
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
ABNT
RAMOS, Jonathan da Silva et al. FINE: improving time and precision of segmentation techniques for vertebral compression fractures in MRI. 2020, Anais.. New York: ACM, 2020. Disponível em: https://doi.org/10.1145/3341105.3374100. Acesso em: 29 dez. 2025. -
APA
Ramos, J. da S., Cazzolato, M. T., Nogueira-Barbosa, M. H., & Traina, A. J. M. (2020). FINE: improving time and precision of segmentation techniques for vertebral compression fractures in MRI. In Proceedings. New York: ACM. doi:10.1145/3341105.3374100 -
NLM
Ramos J da S, Cazzolato MT, Nogueira-Barbosa MH, Traina AJM. FINE: improving time and precision of segmentation techniques for vertebral compression fractures in MRI [Internet]. Proceedings. 2020 ;[citado 2025 dez. 29 ] Available from: https://doi.org/10.1145/3341105.3374100 -
Vancouver
Ramos J da S, Cazzolato MT, Nogueira-Barbosa MH, Traina AJM. FINE: improving time and precision of segmentation techniques for vertebral compression fractures in MRI [Internet]. Proceedings. 2020 ;[citado 2025 dez. 29 ] Available from: https://doi.org/10.1145/3341105.3374100 - 3DBGrowth: volumetric vertebrae segmentation and reconstruction in magnetic resonance imaging
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Informações sobre o DOI: 10.1145/3341105.3374100 (Fonte: oaDOI API)
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