Intraoperative Liver Surface Completion with Graph Convolutional VAE
This addresses a domain-specific problem for surgical planning in laparoscopic liver surgery, with incremental improvements over existing methods.
The paper tackles the problem of predicting the complete liver surface from partial intraoperative point clouds, proposing a graph convolutional VAE method that outperforms a state-of-the-art rigid registration algorithm in visible areas.
In this work we propose a method based on geometric deep learning to predict the complete surface of the liver, given a partial point cloud of the organ obtained during the surgical laparoscopic procedure. We introduce a new data augmentation technique that randomly perturbs shapes in their frequency domain to compensate the limited size of our dataset. The core of our method is a variational autoencoder (VAE) that is trained to learn a latent space for complete shapes of the liver. At inference time, the generative part of the model is embedded in an optimisation procedure where the latent representation is iteratively updated to generate a model that matches the intraoperative partial point cloud. The effect of this optimisation is a progressive non-rigid deformation of the initially generated shape. Our method is qualitatively evaluated on real data and quantitatively evaluated on synthetic data. We compared with a state-of-the-art rigid registration algorithm, that our method outperformed in visible areas.