Computer Aided Medical Procedures, Technische Universität München, Germany.
Computer Aided Medical Procedures, Technische Universität München, Germany.
Med Image Anal. 2016 Dec;34:82-100. doi: 10.1016/j.media.2016.05.003. Epub 2016 May 13.
Real-time visual tracking of a surgical instrument holds great potential for improving the outcome of retinal microsurgery by enabling new possibilities for computer-aided techniques such as augmented reality and automatic assessment of instrument manipulation. Due to high magnification and illumination variations, retinal microsurgery images usually entail a high level of noise and appearance changes. As a result, real-time tracking of the surgical instrument remains challenging in in-vivo sequences. To overcome these problems, we present a method that builds on random forests and addresses the task by modelling the instrument as an articulated object. A multi-template tracker reduces the region of interest to a rectangular area around the instrument tip by relating the movement of the instrument to the induced changes on the image intensities. Within this bounding box, a gradient-based pose estimation infers the location of the instrument parts from image features. In this way, the algorithm does not only provide the location of instrument, but also the positions of the tool tips in real-time. Various experiments on a novel dataset comprising 18 in-vivo retinal microsurgery sequences demonstrate the robustness and generalizability of our method. The comparison on two publicly available datasets indicates that the algorithm can outperform current state-of-the art.
实时视觉跟踪手术器械在提高视网膜微创手术的效果方面具有很大的潜力,因为它为计算机辅助技术(如增强现实和器械操作的自动评估)提供了新的可能性。由于高倍率和光照变化,视网膜微创手术图像通常存在较高的噪声和外观变化。因此,在体内序列中实时跟踪手术器械仍然具有挑战性。为了克服这些问题,我们提出了一种基于随机森林的方法,通过将器械建模为一个铰接物体来解决这个任务。一个多模板跟踪器通过将器械的运动与图像强度上的诱导变化相关联,将感兴趣的区域缩小到器械尖端周围的矩形区域。在这个边界框内,基于梯度的姿势估计从图像特征推断出器械部件的位置。通过这种方式,该算法不仅提供了器械的位置,还实时提供了工具尖端的位置。在一个包含 18 个体内视网膜微创手术序列的新数据集上进行的各种实验证明了我们方法的鲁棒性和通用性。在两个公开可用的数据集上的比较表明,该算法可以优于当前的最先进技术。