ArXiv Preprint
Purpose: In laparoscopic liver surgery (LLS), pre-operative information can
be overlaid onto the intra-operative scene by registering a 3D pre-operative
model to the intra-operative partial surface reconstructed from the
laparoscopic video. To assist with this task, we explore the use of
learning-based feature descriptors, which, to our best knowledge, have not been
explored for use in laparoscopic liver registration. Furthermore, a dataset to
train and evaluate the use of learning-based descriptors does not exist.
Methods: We present the LiverMatch dataset consisting of 16 preoperative
models and their simulated intra-operative 3D surfaces. We also propose the
LiverMatch network designed for this task, which outputs per-point feature
descriptors, visibility scores, and matched points.
Results: We compare the proposed LiverMatch network with anetwork closest to
LiverMatch, and a histogram-based 3D descriptor on the testing split of the
LiverMatch dataset, which includes two unseen pre-operative models and 1400
intra-operative surfaces. Results suggest that our LiverMatch network can
predict more accurate and dense matches than the other two methods and can be
seamlessly integrated with a RANSAC-ICP-based registration algorithm to achieve
an accurate initial alignment.
Conclusion: The use of learning-based feature descriptors in LLR is
promising, as it can help achieve an accurate initial rigid alignment, which,
in turn, serves as an initialization for subsequent non-rigid registration. We
will release the dataset and code upon acceptance.
Zixin Yang, Richard Simon, Cristian A. Linte
2022-11-07
