  Tracking the pose of instruments is a central problem in image-guided
surgery. For microscopic scenarios, optical coherence tomography (OCT) is
increasingly used as an imaging modality. OCT is suitable for accurate pose
estimation due to its micrometer range resolution and volumetric field of view.
However, OCT image processing is challenging due to speckle noise and
reflection artifacts in addition to the images' 3D nature. We address pose
estimation from OCT volume data with a new deep learning-based tracking
framework. For this purpose, we design a new 3D convolutional neural network
(CNN) architecture to directly predict the 6D pose of a small marker geometry
from OCT volumes. We use a hexapod robot to automatically acquire labeled data
points which we use to train 3D CNN architectures for multi-output regression.
We use this setup to provide an in-depth analysis on deep learning-based pose
estimation from volumes. Specifically, we demonstrate that exploiting volume
information for pose estimation yields higher accuracy than relying on 2D
representations with depth information. Supporting this observation, we provide
quantitative and qualitative results that 3D CNNs effectively exploit the depth
structure of marker objects. Regarding the deep learning aspect, we present
efficient design principles for 3D CNNs, making use of insights from the 2D
deep learning community. In particular, we present Inception3D as a new
architecture which performs best for our application. We show that our deep
learning approach reaches errors at our ground-truth label's resolution. We
achieve a mean average error of $\SI{14.89 \pm 9.3}{\micro\metre}$ and
$\SI{0.096 \pm 0.072}{\degree}$ for position and orientation learning,
respectively.
