Publicação
Segmentation of the bone structure from MRI Knee Joint - A use case
| Resumo: | Manual and automatic segmentation techniques can be applied to DICOM medical images from magnetic resonance imaging (MRI) to extract certain structures, such as soft tissues, but the precise extraction of bone structures may be limited. This study studies these types of knee bone tissue segmentation on MRI, to avoid the need to resort to computed tomography (CT) for obtaining the desired bone structures. Manual segmentation was done using ITK-Snap and automatic segmentation algorithms were applied in Python and the ITK library. As a result of this study, it was found that although manual segmentation allowed for precise and consistent identification of the femur, tibia, fibula, and patella, the automatic segmentation needed to achieve the same level of accuracy. |
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| Autores principais: | Silva, Vasco |
| Outros Autores: | Vilaça, Adélio; Veloso, Rita; Coelho, Luís; Magalhães, Renato; Magalhães, Renato |
| Assunto: | Magnetic resonance imaging (MRI) DICOM |
| Ano: | 2024 |
| País: | Portugal |
| Tipo de documento: | comunicação em conferência |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Instituto Politécnico do Porto |
| Idioma: | inglês |
| Origem: | Repositório Científico do Instituto Politécnico do Porto |
| Resumo: | Manual and automatic segmentation techniques can be applied to DICOM medical images from magnetic resonance imaging (MRI) to extract certain structures, such as soft tissues, but the precise extraction of bone structures may be limited. This study studies these types of knee bone tissue segmentation on MRI, to avoid the need to resort to computed tomography (CT) for obtaining the desired bone structures. Manual segmentation was done using ITK-Snap and automatic segmentation algorithms were applied in Python and the ITK library. As a result of this study, it was found that although manual segmentation allowed for precise and consistent identification of the femur, tibia, fibula, and patella, the automatic segmentation needed to achieve the same level of accuracy. |
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