Non-Rigid Volume to Surface Registration using a Data-Driven Biomechanical Model

• URL: http://arxiv.org/abs/2005.14695v1
• Date: Fri, 29 May 2020 17:35:23 GMT
• Title: Non-Rigid Volume to Surface Registration using a Data-Driven Biomechanical Model
• Authors: Micha Pfeiffer, Carina Riediger, Stefan Leger, Jens-Peter K\"uhn, Danilo Seppelt, Ralf-Thorsten Hoffmann, J\"urgen Weitz and Stefanie Speidel
• Abstract summary: We train a convolutional neural network to perform both the search for surface correspondences and the non-rigid registration in one step. The network is trained on physically accurate biomechanical simulations of randomly generated, deforming organ-like structures. We show that the network translates well to real data while maintaining a high inference speed.
• Score: 0.028144129864580446
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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