  We introduce the task of stereo video reconstruction or, equivalently,
2D-to-3D video conversion for minimally invasive surgical video. We design and
implement a series of end-to-end U-Net-based solutions for this task by varying
the input (single frame vs. multiple consecutive frames), loss function (MSE,
MAE, or perceptual losses), and network architecture. We evaluate these
solutions by surveying ten experts - surgeons who routinely perform endoscopic
surgery. We run two separate reader studies: one evaluating individual frames
and the other evaluating fully reconstructed 3D video played on a VR headset.
In the first reader study, a variant of the U-Net that takes as input multiple
consecutive video frames and outputs the missing view performs best. We draw
two conclusions from this outcome. First, motion information coming from
multiple past frames is crucial in recreating stereo vision. Second, the
proposed U-Net variant can indeed exploit such motion information for solving
this task. The result from the second study further confirms the effectiveness
of the proposed U-Net variant. The surgeons reported that they could
successfully perceive depth from the reconstructed 3D video clips. They also
expressed a clear preference for the reconstructed 3D video over the original
2D video. These two reader studies strongly support the usefulness of the
proposed task of stereo reconstruction for minimally invasive surgical video
and indicate that deep learning is a promising approach to this task. Finally,
we identify two automatic metrics, LPIPS and DISTS, that are strongly
correlated with expert judgement and that could serve as proxies for the latter
in future studies.
