Deformation estimation of an elastic object by partial observation using a neural network

TL;DR

This paper presents a neural network-based method to estimate the full 3D deformation of elastic objects, like human organs, from very limited observation points, aiding surgical navigation.

Contribution

Introduces a novel neural network approach for estimating entire elastic object deformation from sparse observation data, demonstrated on models including a human liver.

Findings

Average error of 0.041 mm in liver deformation estimation

Achieved accurate deformation estimation from only 3% observations

Method effective for deformations up to 66.4 mm

Abstract

Deformation estimation of elastic object assuming an internal organ is important for the computer navigation of surgery. The aim of this study is to estimate the deformation of an entire three-dimensional elastic object using displacement information of very few observation points. A learning approach with a neural network was introduced to estimate the entire deformation of an object. We applied our method to two elastic objects; a rectangular parallelepiped model, and a human liver model reconstructed from computed tomography data. The average estimation error for the human liver model was 0.041 mm when the object was deformed up to 66.4 mm, from only around 3 % observations. These results indicate that the deformation of an entire elastic object can be estimated with an acceptable level of error from limited observations by applying a trained neural network to a new deformation.