Abstract：
OBJECTIVE: Surgical robotics tends to develop cognitive control architectures to provide certain degree of autonomy to improve patient safety and surgery outcomes, while decreasing the required surgeons\' cognitive load dedicated to low level decisions. Cognition needs workspace perception, which is an essential step towards automatic decision-making and task planning capabilities. Robust and accurate detection and tracking in minimally invasive surgery suffers from limited visibility, occlusions, anatomy deformations and camera movements.
METHODS: This paper develops a robust methodology to detect and track anatomical structures in real time to be used in automatic control of robotic systems and augmented reality. The work focuses on the experimental validation in highly challenging surgery: fetoscopic repair of Open Spina Bifida. The proposed method is based on two sequential steps: first, selection of relevant points (contour) using a Convolutional Neural Network and, second, reconstruction of the anatomical shape by means of deformable geometric primitives.
RESULTS: The methodology performance was validated with different scenarios. Synthetic scenario tests, designed for extreme validation conditions, demonstrate the safety margin offered by the methodology with respect to the nominal conditions during surgery. Real scenario experiments have demonstrated the validity of the method in terms of accuracy, robustness and computational efficiency.
CONCLUSIONS: This paper presents a robust anatomical structure detection in present of abrupt camera movements, severe occlusions and deformations. Even though the paper focuses on a case study, Open Spina Bifida, the methodology is applicable in all anatomies which contours can be approximated by geometric primitives. The methodology is designed to provide effective inputs to cognitive robotic control and augmented reality systems that require accurate tracking of sensitive anatomies.
METHODS: This paper develops a robust methodology to detect and track anatomical structures in real time to be used in automatic control of robotic systems and augmented reality. The work focuses on the experimental validation in highly challenging surgery: fetoscopic repair of Open Spina Bifida. The proposed method is based on two sequential steps: first, selection of relevant points (contour) using a Convolutional Neural Network and, second, reconstruction of the anatomical shape by means of deformable geometric primitives.
RESULTS: The methodology performance was validated with different scenarios. Synthetic scenario tests, designed for extreme validation conditions, demonstrate the safety margin offered by the methodology with respect to the nominal conditions during surgery. Real scenario experiments have demonstrated the validity of the method in terms of accuracy, robustness and computational efficiency.
CONCLUSIONS: This paper presents a robust anatomical structure detection in present of abrupt camera movements, severe occlusions and deformations. Even though the paper focuses on a case study, Open Spina Bifida, the methodology is applicable in all anatomies which contours can be approximated by geometric primitives. The methodology is designed to provide effective inputs to cognitive robotic control and augmented reality systems that require accurate tracking of sensitive anatomies.
摘要：
目的：手术机器人倾向于开发认知控制架构，以提供一定程度的自主性，以提高患者安全性和手术效果，同时减少所需的外科医生的认知负荷致力于低级决策。认知需要工作空间感知，这是实现自动决策和任务计划能力的重要一步。在微创手术中，强大而准确的检测和跟踪受到可见性有限的影响，闭塞,解剖变形和相机运动。
方法：本文开发了一种鲁棒的方法来实时检测和跟踪解剖结构，以用于机器人系统的自动控制和增强现实。这项工作的重点是在极具挑战性的手术实验验证：开放脊柱裂的胎儿镜修复。所提出的方法基于两个顺序步骤：首先，使用卷积神经网络选择相关点(轮廓)，第二,通过可变形的几何图元重建解剖形状。
结果：用不同的方案验证了方法性能。综合场景测试，专为极端验证条件而设计，证明该方法在手术过程中相对于标称条件提供的安全裕度。真实场景实验证明了该方法在准确性方面的有效性，鲁棒性和计算效率。
结论：本文提出了一种针对摄像机突然运动的强大解剖结构检测，严重闭塞和变形。尽管论文的重点是案例研究，打开脊柱裂,该方法适用于所有可以通过几何图元近似轮廓的解剖结构。该方法旨在为需要精确跟踪敏感解剖结构的认知机器人控制和增强现实系统提供有效的输入。
方法：本文开发了一种鲁棒的方法来实时检测和跟踪解剖结构，以用于机器人系统的自动控制和增强现实。这项工作的重点是在极具挑战性的手术实验验证：开放脊柱裂的胎儿镜修复。所提出的方法基于两个顺序步骤：首先，使用卷积神经网络选择相关点(轮廓)，第二,通过可变形的几何图元重建解剖形状。
结果：用不同的方案验证了方法性能。综合场景测试，专为极端验证条件而设计，证明该方法在手术过程中相对于标称条件提供的安全裕度。真实场景实验证明了该方法在准确性方面的有效性，鲁棒性和计算效率。
结论：本文提出了一种针对摄像机突然运动的强大解剖结构检测，严重闭塞和变形。尽管论文的重点是案例研究，打开脊柱裂,该方法适用于所有可以通过几何图元近似轮廓的解剖结构。该方法旨在为需要精确跟踪敏感解剖结构的认知机器人控制和增强现实系统提供有效的输入。
