  X-Ray imaging is quick, cheap and useful for front-line care assessment and
intra-operative real-time imaging (e.g., C-Arm Fluoroscopy). However, it
suffers from projective information loss and lacks vital volumetric information
on which many essential diagnostic biomarkers are based on. In this paper we
explore probabilistic methods to reconstruct 3D volumetric images from 2D
imaging modalities and measure the models' performance and confidence. We show
our models' performance on large connected structures and we test for
limitations regarding fine structures and image domain sensitivity. We utilize
fast end-to-end training of a 2D-3D convolutional networks, evaluate our method
on 117 CT scans segmenting 3D structures from digitally reconstructed
radiographs (DRRs) with a Dice score of $0.91 \pm 0.0013$. Source code will be
made available by the time of the conference.
