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

• URL: http://arxiv.org/abs/2004.10121v2
• Date: Mon, 1 Jun 2020 10:53:43 GMT
• Title: A Deep Learning Approach for Motion Forecasting Using 4D OCT Data
• Authors: Marcel Bengs and Nils Gessert and Alexander Schlaefer
• Abstract summary: We propose 4D-temporal deep learning for end-to-end motion forecasting and estimation using a stream of OCT volumes. Our best performing 4D method achieves motion forecasting with an overall average correlation of 97.41%, while also improving motion estimation performance by a factor of 2.5 compared to a previous 3D approach.
• Score: 69.62333053044712
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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