In this work we explore methods of performing 3D segmentation using single-plane 2D projections of target segmentation maps for supervision. Specifically, we attempt to segment the spine in 3D MR scans using annotations derived from registered 2D coronal DXA scans of the same patient. By exploiting prior knowledge of the 3D shape and appearance of the spine, we propose several methods to perform this task. We test these methods empirically using DXA-Dixon MRI scan pairs from the UK Biobank. The best-performing segmentation model achieves good agreement with manual 3D annotations, with a 3D Dice score of 0.642. By performing this segmentation, one can estimate the 3D curve of the spine, which has been shown to improve monitoring and prediction of scoliosis progression.