22 research outputs found
A Deep Learning Approach for Motion Forecasting Using 4D OCT Data
Forecasting motion of a specific target object is a common problem for
surgical interventions, e.g. for localization of a target region, guidance for
surgical 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-based
motion estimation based on two volumetric images. We extend this approach and
investigate whether using a time series of volumes enables motion forecasting.
We propose 4D spatio-temporal deep learning for end-to-end motion forecasting
and estimation using a stream of OCT volumes. We design and evaluate five
different 3D and 4D deep learning methods using a tissue data set. Our best
performing 4D method achieves motion forecasting with an overall average
correlation coefficient of 97.41%, while also improving motion estimation
performance by a factor of 2.5 compared to a previous 3D approach.Comment: Accepted for publication at MIDL 2020:
https://openreview.net/forum?id=WVd56kgR