@inproceedings{bengs2020deep,
Author = {M. Bengs and N. Gessert and A. Schlaefer},
Title = {A Deep Learning Approach for Motion Forecasting Using 4D OCT Data.},
Year = {(2020).},
Pages = {2004.10121},
Booktitle = {<em>International Conference on Medical Imaging with Deep Learning</em>},
Url = {https://arxiv.org/abs/2004.10121},
Abstract = {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{Bibtex file generated on 2026-5-12 with typo3 si_bibtex plugin. Data from https://www.tuhh.de/mtec/publications/2024-2020 }