  Non-rigid registration is a key component in soft-tissue navigation. We focus
on laparoscopic liver surgery, where we register the organ model obtained from
a preoperative CT scan to the intraoperative partial organ surface,
reconstructed from the laparoscopic video. This is a challenging task due to
sparse and noisy intraoperative data, real-time requirements and many unknowns
- such as tissue properties and boundary conditions. Furthermore, establishing
correspondences between pre- and intraoperative data can be extremely difficult
since the liver usually lacks distinct surface features and the used imaging
modalities suffer from very different types of noise. In this work, we train a
convolutional neural network to perform both the search for surface
correspondences as well as the non-rigid registration in one step. The network
is trained on physically accurate biomechanical simulations of randomly
generated, deforming organ-like structures. This enables the network to
immediately generalize to a new patient organ without the need to re-train. We
add various amounts of noise to the intraoperative surfaces during training,
making the network robust to noisy intraoperative data. During inference, the
network outputs the displacement field which matches the preoperative volume to
the partial intraoperative surface. In multiple experiments, we show that the
network translates well to real data while maintaining a high inference speed.
Our code is made available online.
