BEAUT: a radiomic approach to identify potential lumbar fractures in magnetic resonance imaging (2021)
- Authors:
- USP affiliated authors: TRAINA JUNIOR, CAETANO - ICMC ; BARBOSA, MARCELLO HENRIQUE NOGUEIRA - FMRP ; TRAINA, AGMA JUCI MACHADO - ICMC ; RAMOS, JONATHAN DA SILVA - ICMC ; MACIEL, JAMILLY GOMES - FMRP ; CAZZOLATO, MIRELA TEIXEIRA - ICMC
- Unidades: ICMC; FMRP
- DOI: 10.1109/CBMS52027.2021.00089
- Subjects: APRENDIZADO COMPUTACIONAL; RECONHECIMENTO DE IMAGEM; TECNOLOGIAS DA SAÚDE; RESSONÂNCIA MAGNÉTICA; DENSITOMETRIA ÓSSEA; OSTEOPOROSE; FRATURAS; VÉRTEBRAS LOMBARES
- Keywords: Magnetic resonance imaging,; machine learning; osteoporotic fractures; texture analysis
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Publisher: IEEE
- Publisher place: Los Alamitos
- Date published: 2021
- Source:
- Título: Proceedings
- ISSN: 2372-9198
- Conference titles: International Symposium on Computer-Based Medical Systems - CBMS
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
ABNT
RAMOS, Jonathan da Silva et al. BEAUT: a radiomic approach to identify potential lumbar fractures in magnetic resonance imaging. 2021, Anais.. Los Alamitos: IEEE, 2021. Disponível em: https://doi.org/10.1109/CBMS52027.2021.00089. Acesso em: 28 dez. 2025. -
APA
Ramos, J. da S., Maciel, J. G., Cazzolato, M. T., Traina Junior, C., Nogueira-Barbosa, M. H., & Traina, A. J. M. (2021). BEAUT: a radiomic approach to identify potential lumbar fractures in magnetic resonance imaging. In Proceedings. Los Alamitos: IEEE. doi:10.1109/CBMS52027.2021.00089 -
NLM
Ramos J da S, Maciel JG, Cazzolato MT, Traina Junior C, Nogueira-Barbosa MH, Traina AJM. BEAUT: a radiomic approach to identify potential lumbar fractures in magnetic resonance imaging [Internet]. Proceedings. 2021 ;[citado 2025 dez. 28 ] Available from: https://doi.org/10.1109/CBMS52027.2021.00089 -
Vancouver
Ramos J da S, Maciel JG, Cazzolato MT, Traina Junior C, Nogueira-Barbosa MH, Traina AJM. BEAUT: a radiomic approach to identify potential lumbar fractures in magnetic resonance imaging [Internet]. Proceedings. 2021 ;[citado 2025 dez. 28 ] Available from: https://doi.org/10.1109/CBMS52027.2021.00089 - FINE: improving time and precision of segmentation techniques for vertebral compression fractures in MRI
- Fast and accurate 3-D spine MRI segmentation using FastCleverSeg
- 3DBGrowth: volumetric vertebrae segmentation and reconstruction in magnetic resonance imaging
- Spine MRI texture analysis and prediction of osteoporotic vertebral fracture
- Fast and smart segmentation of paraspinal muscles in magnetic resonance imaging with CleverSeg
- Analysis of vertebrae without fracture on spine MRI to assess bone fragility: a comparison of traditional machine learning and deep learning
- Exploratory data analysis in electronic health records graphs: intuitive features and visualization tools
- Wia-Spine: a CBIR environment with embedded radiomic features to assess fragility fractures
- Establishing trajectories of moving objects without identities: the intricacies of cell tracking and a solution
- Combining semantic graph features and a common data model to exploit the interoperability of patient databases
Informações sobre o DOI: 10.1109/CBMS52027.2021.00089 (Fonte: oaDOI API)
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