Segmentation of the Bone Structure from MRI Knee Joint - A Use Case

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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Publication Details

Published
2024-11-01
DOI
https://doi.org/10.17979/spudc.9788497498913.12
Primary Topic
Medical Imaging and Analysis
Type
article
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article

Segmentation of the Bone Structure from MRI Knee Joint - A Use Case

Adélio Vilaça, Renato Magalhães, Rita Veloso, Victor Ferreira da Silva et al.
Medical Imaging and Analysis
article

Segmentation of the Bone Structure from MRI Knee Joint - A Use Case

Adélio Vilaça, Renato Magalhães, Rita Veloso, Victor Ferreira da Silva, Luís Coelho
article en

Abstract

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.

Openalex Percentile: Top 76%
Medical Imaging and Analysis
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