  In robotic laser surgery, shape prediction of an one-shot ablation cavity is
an important problem for minimizing errant overcutting of healthy tissue during
the course of pathological tissue resection and precise tumor removal. Since it
is difficult to physically model the laser-tissue interaction due to the
variety of optical tissue properties, complicated process of heat transfer, and
uncertainty about the chemical reaction, we propose a 3D cavity prediction
model based on an entirely data-driven method without any assumptions of laser
settings and tissue properties. Based on the cavity prediction model, we
formulate a novel robotic laser planning problem to determine the optimal laser
incident configuration, which aims to create a cavity that aligns with the
surface target (e.g. tumor, pathological tissue).
  To solve the one-shot ablation cavity prediction problem, we model the 3D
geometric relation between the tissue surface and the laser energy profile as a
non-linear regression problem that can be represented by a single-layer
perceptron (SLP) network. The SLP network is encoded in a novel kinematic model
to predict the shape of the post-ablation cavity with an arbitrary laser input.
To estimate the SLP network parameters, we formulate a dataset of one-shot
laser-phantom cavities reconstructed by the optical coherence tomography (OCT)
B-scan images for the data-driven modelling. To verify the method. The learned
cavity prediction model is applied to solve a simplified robotic laser planning
problem modelled as a surface alignment error minimization problem. The initial
results report (91.1 +- 3.0)% 3D-cavity-Intersection-over-Union (3D-cavity-IoU)
for the 3D cavity prediction and an average of 97.9% success rate for the
simulated surface alignment experiments.
