论文标题
LapseG3D：代表腹腔镜场景的点云的弱监督语义分割
LapSeg3D: Weakly Supervised Semantic Segmentation of Point Clouds Representing Laparoscopic Scenes
论文作者
论文摘要
手术场景的语义分割是机器人辅助干预措施中任务自动化的先决条件。我们提出了LapseG3D，这是一种基于DNN的新型方法，用于代表手术场景的点云的体素注释。由于训练数据的手动注释非常耗时，因此我们引入了基于半自治的基于聚类的管道，用于胆囊的注释，该管道用于为DNN生成分段标签。当对手动注释数据进行评估时，LapseG3D在前体猪肝的各种数据集上的胆囊分割达到了0.94的F1得分。我们显示LapseG3D可以准确地跨越具有不同RGB-D摄像头系统记录的不同胆囊和数据集。
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.
