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

TL;DR

This paper introduces a 4D deep learning framework for motion forecasting in OCT imaging, significantly improving accuracy over previous 3D methods by leveraging temporal volume sequences.

Contribution

It extends existing OCT-based motion estimation to 4D spatio-temporal deep learning, enabling more accurate motion forecasting in surgical applications.

Findings

Achieves 97.41% correlation coefficient in motion forecasting.

Improves motion estimation performance by 2.5 times over 3D methods.

Evaluates five different deep learning architectures for OCT data.

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.