Richa Rogério, Bó Antônio P L, Poignet Philippe
LIRMM - UMR 5506 CNRS - UM2, 161 Rue Ada 34392 Montpellier, France.
Med Image Comput Comput Assist Interv. 2010;13(Pt 1):267-74. doi: 10.1007/978-3-642-15705-9_33.
In the context of minimally invasive cardiac surgery, active vision-based motion compensation schemes have been proposed for mitigating problems related to physiological motion. However, robust and accurate visual tracking is a difficult task. The purpose of this paper is to present a hybrid tracker that estimates the heart surface deformation using the outputs of multiple visual tracking techniques. In the proposed method, the failure of an individual technique can be circumvented by the success of others, enabling the robust estimation of the heart surface deformation with increased spatial resolution. In addition, for coping with the absence of visual information due to motion blur or occlusions, a temporal heart motion model is incorporated as an additional support for the visual tracking task. The superior performance of the proposed technique compared to existing techniques individually is demonstrated through experiments conducted on recorded images of an in vivo minimally invasive CABG using the DaVinci robotic platform.
在微创心脏手术的背景下，已经提出了基于主动视觉的运动补偿方案，以减轻与生理运动相关的问题。然而，稳健而准确的视觉跟踪是一项艰巨的任务。本文的目的是提出一种混合跟踪器，该跟踪器使用多种视觉跟踪技术的输出估计心脏表面变形。在所提出的方法中，个别技术的失败可以通过其他技术的成功来规避，从而能够以更高的空间分辨率稳健地估计心脏表面变形。此外，为了应对由于运动模糊或遮挡导致的视觉信息缺失，引入了一个时间心脏运动模型作为视觉跟踪任务的额外支持。通过使用达芬奇机器人平台对体内微创冠状动脉旁路移植术的记录图像进行实验，证明了所提出技术相对于现有技术各自的优越性能。
