A Calibration Approach for Elasticity Estimation with Medical Tools

Soft tissue elasticity is directly related to different stages of diseasesand can be used for tissue identification during minimally invasive procedures.By palpating a tissue with a robot in a minimally invasive fashionforce-displacement curves can be acquired. However, force-displacement curvesstrongly depend on the tool geometry which is often complex in the case ofmedical tools. Hence, a tool calibration procedure is desired to directly mapforce-displacement curves to the corresponding tissue elasticity.We present anexperimental setup for calibrating medical tools with a robot. First, wepropose to estimate the elasticity of gelatin phantoms by spherical indentationwith a state-of-the-art contact model. We estimate force-displacement curvesfor different gelatin elasticities and temperatures. Our experimentsdemonstrate that gelatin elasticity is highly dependent on temperature, whichcan lead to an elasticity offset if not considered. Second, we propose to use amore complex material model, e.g., a neural network, that can be trained withthe determined elasticities. Considering the temperature of the gelatin samplewe can represent different elasticities per phantom and thereby increase ourtraining data.We report elasticity values ranging from 10 to 40 kPa for a 10gelatin phantom, depending on temperature.