论文标题
语义杀手：内窥镜组织识别，重建和跟踪的语义感知手术感知框架
Semantic-SuPer: A Semantic-aware Surgical Perception Framework for Endoscopic Tissue Identification, Reconstruction, and Tracking
论文作者
论文摘要
手术场景的准确，健壮的跟踪和重建是对自动机器人手术的至关重要的技术。现有的手术3D感知算法主要依赖于几何信息，而我们也建议使用图像分割算法从内窥镜视频中推断出的语义信息。在本文中，我们介绍了一个新颖的，全面的外科感知框架，语义效力，该框架整合了几何和语义信息，以促进数据关联，3D重建以及内窥镜场景的跟踪，从而使下游任务受益于外科手术导航。提出的框架在具有变形组织的具有挑战性的内窥镜数据上证明了其优势，其优势与我们的基线和其他几种最先进的方法。我们的代码和数据集可从https://github.com/ucsdarclab/python-super获得。
Accurate and robust tracking and reconstruction of the surgical scene is a critical enabling technology toward autonomous robotic surgery. Existing algorithms for 3D perception in surgery mainly rely on geometric information, while we propose to also leverage semantic information inferred from the endoscopic video using image segmentation algorithms. In this paper, we present a novel, comprehensive surgical perception framework, Semantic-SuPer, that integrates geometric and semantic information to facilitate data association, 3D reconstruction, and tracking of endoscopic scenes, benefiting downstream tasks like surgical navigation. The proposed framework is demonstrated on challenging endoscopic data with deforming tissue, showing its advantages over our baseline and several other state-of the-art approaches. Our code and dataset are available at https://github.com/ucsdarclab/Python-SuPer.
