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Forecasting motion of a specific target object is a common problem forsurgical interventions, e.g. for localization of a target region, guidance forsurgical interventions, or motion compensation. Optical coherence tomography(OCT) is an imaging modality with a high spatial and temporal resolution.Recently, deep learning methods have shown promising performance for OCT-basedmotion estimation based on two volumetric images. We extend this approach andinvestigate whether using a time series of volumes enables motion forecasting.We propose 4D spatio-temporal deep learning for end-to-end motion forecastingand estimation using a stream of OCT volumes. We design and evaluate fivedifferent 3D and 4D deep learning methods using a tissue data set. Our bestperforming 4D method achieves motion forecasting with an overall averagecorrelation coefficient of 97.41%, while also improving motion estimationperformance by a factor of 2.5 compared to a previous 3D approach.

A Deep Learning Approach for Motion Forecasting Using 4D OCT Data