  In recent times there is a growing development of video based applications
for surgical purposes. Part of these applications can work offline after the
end of the procedure, other applications must react immediately. However, there
are cases where the response should be done during the procedure but some delay
is acceptable. In the literature, the online-offline performance gap is known.
Our goal in this study was to learn the performance-delay trade-off and design
an MS-TCN++-based algorithm that can utilize this trade-off. To this aim, we
used our open surgery simulation data-set containing 96 videos of 24
participants that perform a suturing task on a variable tissue simulator. In
this study, we used video data captured from the side view. The Networks were
trained to identify the performed surgical gestures. The naive approach is to
reduce the MS-TCN++ depth, as a result, the receptive field is reduced, and
also the number of required future frames is also reduced. We showed that this
method is sub-optimal, mainly in the small delay cases. The second method was
to limit the accessible future in each temporal convolution. This way, we have
flexibility in the network design and as a result, we achieve significantly
better performance than in the naive approach.
