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Purpose: In laparoscopic liver surgery (LLS), pre-operative information canbe overlaid onto the intra-operative scene by registering a 3D pre-operativemodel to the intra-operative partial surface reconstructed from thelaparoscopic video. To assist with this task, we explore the use oflearning-based feature descriptors, which, to our best knowledge, have not beenexplored for use in laparoscopic liver registration. Furthermore, a dataset totrain and evaluate the use of learning-based descriptors does not exist.  Methods: We present the LiverMatch dataset consisting of 16 preoperativemodels and their simulated intra-operative 3D surfaces. We also propose theLiverMatch network designed for this task, which outputs per-point featuredescriptors, visibility scores, and matched points.  Results: We compare the proposed LiverMatch network with anetwork closest toLiverMatch, and a histogram-based 3D descriptor on the testing split of theLiverMatch dataset, which includes two unseen pre-operative models and 1400intra-operative surfaces. Results suggest that our LiverMatch network canpredict more accurate and dense matches than the other two methods and can beseamlessly integrated with a RANSAC-ICP-based registration algorithm to achievean accurate initial alignment.  Conclusion: The use of learning-based feature descriptors in LLR ispromising, as it can help achieve an accurate initial rigid alignment, which,in turn, serves as an initialization for subsequent non-rigid registration. Wewill release the dataset and code upon acceptance.

Learning Feature Descriptors for Pre- and Intra-operative Point Cloud Matching for Laparoscopic Liver Registration