3D Spine Shape Estimation from Single 2D DXA
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866913594342375424 |
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| author | Bourigault, Emmanuelle Jamaludin, Amir Zisserman, Andrew |
| author_facet | Bourigault, Emmanuelle Jamaludin, Amir Zisserman, Andrew |
| contents | Scoliosis is traditionally assessed based solely on 2D lateral deviations, but recent studies have also revealed the importance of other imaging planes in understanding the deformation of the spine. Consequently, extracting the spinal geometry in 3D would help quantify these spinal deformations and aid diagnosis. In this study, we propose an automated general framework to estimate the 3D spine shape from 2D DXA scans. We achieve this by explicitly predicting the sagittal view of the spine from the DXA scan. Using these two orthogonal projections of the spine (coronal in DXA, and sagittal from the prediction), we are able to describe the 3D shape of the spine. The prediction is learnt from over 30k paired images of DXA and MRI scans. We assess the performance of the method on a held out test set, and achieve high accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_01504 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | 3D Spine Shape Estimation from Single 2D DXA Bourigault, Emmanuelle Jamaludin, Amir Zisserman, Andrew Image and Video Processing Computer Vision and Pattern Recognition Scoliosis is traditionally assessed based solely on 2D lateral deviations, but recent studies have also revealed the importance of other imaging planes in understanding the deformation of the spine. Consequently, extracting the spinal geometry in 3D would help quantify these spinal deformations and aid diagnosis. In this study, we propose an automated general framework to estimate the 3D spine shape from 2D DXA scans. We achieve this by explicitly predicting the sagittal view of the spine from the DXA scan. Using these two orthogonal projections of the spine (coronal in DXA, and sagittal from the prediction), we are able to describe the 3D shape of the spine. The prediction is learnt from over 30k paired images of DXA and MRI scans. We assess the performance of the method on a held out test set, and achieve high accuracy. |
| title | 3D Spine Shape Estimation from Single 2D DXA |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2412.01504 |