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.

本文提出了一个新颖的 LapSeg3D 基于深度学习的方法，用于手术场景的语义分割。通过针对胆囊进行半自动聚类标注管道生成标签，该方法可用于手术场景的自动化任务，其在不同数据集上测试胆囊分割的准确率（F1 score）达到了 0.94，且可适用于不同的 RGB-D 相机系统。