Abstract 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 14.89 ± 9.3 µm and 0.096 ± 0.072° for position and orientation learning, respectively.
Optical coherence tomography (OCT) guided laser cochleostomy enables the ablation accuracy on tens of micrometer scale and therefore also requires a high patient tracking accuracy on the same order of magnitude, which however exceeds the ability of the conventional optical or electromagnetic tracking systems. We have developed a novel approach of using OCT itself as a highly accurate optical tracking system by ablating tiny artificial landmarks surrounding the cochleostomy on the target bone surface and locating them in the online acquired 3D OCT Volumes. The mechanism enables a direct tracking of the target area with respect to the laser optics while the conventional systems rely on indirect tracking of the markers or sensors attached to the regions of interest. The new approach has achieved an overall tracking accuracy of 22.8±14.9μm (mean absolute error) and 27.2μm (root mean square), which is one order of magnitude higher than the conventional tracking system-based setups.