  Soft tissue elasticity is directly related to different stages of diseases
and can be used for tissue identification during minimally invasive procedures.
By palpating a tissue with a robot in a minimally invasive fashion
force-displacement curves can be acquired. However, force-displacement curves
strongly depend on the tool geometry which is often complex in the case of
medical tools. Hence, a tool calibration procedure is desired to directly map
force-displacement curves to the corresponding tissue elasticity.We present an
experimental setup for calibrating medical tools with a robot. First, we
propose to estimate the elasticity of gelatin phantoms by spherical indentation
with a state-of-the-art contact model. We estimate force-displacement curves
for different gelatin elasticities and temperatures. Our experiments
demonstrate that gelatin elasticity is highly dependent on temperature, which
can lead to an elasticity offset if not considered. Second, we propose to use a
more complex material model, e.g., a neural network, that can be trained with
the determined elasticities. Considering the temperature of the gelatin sample
we can represent different elasticities per phantom and thereby increase our
training data.We report elasticity values ranging from 10 to 40 kPa for a 10%
gelatin phantom, depending on temperature.
