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Purpose. Localizing structures and estimating the motion of a specific targetregion are common problems for navigation during surgical interventions.Optical coherence tomography (OCT) is an imaging modality with a high spatialand temporal resolution that has been used for intraoperative imaging and alsofor motion estimation, for example, in the context of ophthalmic surgery orcochleostomy. Recently, motion estimation between a template and a moving OCTimage has been studied with deep learning methods to overcome the shortcomingsof conventional, feature-based methods.  Methods. We investigate whether using a temporal stream of OCT image volumescan improve deep learning-based motion estimation performance. For thispurpose, we design and evaluate several 3D and 4D deep learning methods and wepropose a new deep learning approach. Also, we propose a temporalregularization strategy at the model output.  Results. Using a tissue dataset without additional markers, our deep learningmethods using 4D data outperform previous approaches. The best performing 4Darchitecture achieves an correlation coefficient (aCC) of 98.58% compared to85.0% of a previous 3D deep learning method. Also, our temporal regularizationstrategy at the output further improves 4D model performance to an aCC of99.06%. In particular, our 4D method works well for larger motion and is robusttowards image rotations and motion distortions.  Conclusions. We propose 4D spatio-temporal deep learning for OCT-based motionestimation. On a tissue dataset, we find that using 4D information for themodel input improves performance while maintaining reasonable inference times.Our regularization strategy demonstrates that additional temporal informationis also beneficial at the model output.

Spatio-Temporal Deep Learning Methods for Motion Estimation Using 4D OCT Image Data