Deep learning with 4D spatio-temporal data representations for OCT-based force estimation

TL;DR

This paper introduces a novel 4D spatio-temporal deep learning approach for force estimation in OCT-based minimally-invasive surgery, demonstrating improved accuracy and the potential for short-term force prediction.

Contribution

It extends deep learning-based force estimation to 4D OCT data, compares data representations, and analyzes temporal effects and force prediction capabilities.

Findings

4D approach outperforms lower-dimensional methods with 10.7mN MAE

Temporal information improves force estimation accuracy

Force prediction for safety features is feasible

Abstract

Estimating the forces acting between instruments and tissue is a challenging problem for robot-assisted minimally-invasive surgery. Recently, numerous vision-based methods have been proposed to replace electro-mechanical approaches. Moreover, optical coherence tomography (OCT) and deep learning have been used for estimating forces based on deformation observed in volumetric image data. The method demonstrated the advantage of deep learning with 3D volumetric data over 2D depth images for force estimation. In this work, we extend the problem of deep learning-based force estimation to 4D spatio-temporal data with streams of 3D OCT volumes. For this purpose, we design and evaluate several methods extending spatio-temporal deep learning to 4D which is largely unexplored so far. Furthermore, we provide an in-depth analysis of multi-dimensional image data representations for force estimation, comparing our 4D approach to previous, lower-dimensional methods. Also, we analyze the effect of temporal information and we study the prediction of short-term future force values, which could facilitate safety features. For our 4D force estimation architectures, we find that efficient decoupling of spatial and temporal processing is advantageous. We show that using 4D spatio-temporal data outperforms all previously used data representations with a mean absolute error of 10.7mN. We find that temporal information is valuable for force estimation and we demonstrate the feasibility of force prediction.