bibtext: @InProceedings{ Bou_3D_MICCAI2024,
                   author = { Bourigault, Emmanuelle and Jamaludin, Amir and Zisserman, Andrew },
                   title = { { 3D Spine Shape Estimation from Single 2D DXA } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15005 },
                   month = {October},
                   pages = { pending },
                }
abstract: Scoliosis is currently assessed 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 {\em 3D }spine shape from {\em 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. Our code is available at \href{https://github.com/EmmanuelleB985/DXA_to_3D}{https://github.com/EmmanuelleB985/DXA-to-3D.}
title: 3D Spine Shape Estimation from Single 2D DXA
authors: Bourigault, Emmanuelle Jamaludin, Amir Zisserman, Andrew