Sgouros Nicholas P, Loukas Constantinos, Koufi Vassiliki, Troupis Theodore G, Georgiou Evangelos
National and Kapodistrian University of Athens, School of Medicine, Athens, Greece.
University of Piraeus, Department of Digital Systems, Piraeus, Greece.
Int J Med Robot. 2018 Feb;14(1). doi: 10.1002/rcs.1853. Epub 2017 Aug 15.
Various sensors and methods are used for evaluating trainees' skills in laparoscopic procedures. These methods are usually task-specific and involve high costs or advanced setups.
In this paper, we propose a novel manoeuver representation feature space (MRFS) constructed by tracking the vanishing points of the edges of the graspers on the video sequence frames, acquired by the standard box trainer camera. This study aims to provide task-agnostic classification of trainees in experts and novices using a single MRFS over two basic laparoscopic tasks.
The system achieves an average of 96% correct classification ratio (CCR) when no information on the performed task is available and >98% CCR when the task is known, outperforming a recently proposed video-based technique by >13%.
Robustness, extensibility and accurate task-agnostic classification between novices and experts is achieved by utilizing advanced computer vision techniques and derived features from a novel MRFS.
各种传感器和方法被用于评估学员在腹腔镜手术中的技能。这些方法通常是特定于任务的，并且涉及高成本或先进的设置。
在本文中，我们提出了一种新颖的操作表示特征空间（MRFS），它是通过跟踪标准箱式训练器摄像头采集的视频序列帧上抓持器边缘的消失点构建而成。本研究旨在使用单个MRFS对两项基本腹腔镜任务的学员进行专家和新手的任务无关分类。
当没有关于执行任务的信息时，该系统平均正确分类率（CCR）达到96%，当任务已知时CCR>98%，比最近提出的基于视频的技术性能高出>13%。
通过利用先进的计算机视觉技术和从新颖的MRFS派生的特征，实现了新手和专家之间的鲁棒性、可扩展性和准确的任务无关分类。
