The semantic segmentation of surgical scenes is a prerequisite for task automation in robot assisted interventions. We propose LapSeg3D, a novel DNN-based approach for the voxel-wise annotation of point clouds representing surgical scenes. As the manual annotation of training data is highly time consuming, we introduce a semi-autonomous clustering-based pipeline for the annotation of the gallbladder, which is used to generate segmented labels for the DNN. When evaluated against manually annotated data, LapSeg3D achieves an F1 score of 0.94 for gallbladder segmentation on various datasets of ex-vivo porcine livers. We show LapSeg3D to generalize accurately across different gallbladders and datasets recorded with different RGB-D camera systems.
@article{arxiv.2207.07418,
title = {LapSeg3D: Weakly Supervised Semantic Segmentation of Point Clouds Representing Laparoscopic Scenes},
author = {Benjamin Alt and Christian Kunz and Darko Katic and Rayan Younis and Rainer Jäkel and Beat Peter Müller-Stich and Martin Wagner and Franziska Mathis-Ullrich},
journal= {arXiv preprint arXiv:2207.07418},
year = {2023}
}
Comments
6 pages, 5 figures, accepted at the 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022), Kyoto, Japan