  Automatic motion compensation and adjustment of an intraoperative imaging
modality's field of view is a common problem during interventions. Optical
coherence tomography (OCT) is an imaging modality which is used in
interventions due to its high spatial resolution of few micrometers and its
temporal resolution of potentially several hundred volumes per second. However,
performing motion compensation with OCT is problematic due to its small field
of view which might lead to tracked objects being lost quickly. We propose a
novel deep learning-based approach that directly learns input parameters of
motors that move the scan area for motion compensation from optical coherence
tomography volumes. We design a two-path 3D convolutional neural network (CNN)
architecture that takes two volumes with an object to be tracked as its input
and predicts the necessary motor input parameters to compensate the object's
movement. In this way, we learn the calibration between object movement and
system parameters for motion compensation with arbitrary objects. Thus, we
avoid error-prone hand-eye calibration and handcrafted feature tracking from
classical approaches. We achieve an average correlation coefficient of 0.998
between predicted and ground-truth motor parameters which leads to sub-voxel
accuracy. Furthermore, we show that our deep learning model is real-time
capable for use with the system's high volume acquisition frequency.
