Stoyanov Danail
Centre for Medical Image Computing, University College London, WC1E 8BT, UK.
Med Image Comput Comput Assist Interv. 2012;15(Pt 1):479-86. doi: 10.1007/978-3-642-33415-3_59.
Information about the 3D shape and motion of tissue surfaces at the surgical site during minimally invasive surgery is important for providing metric measurements that enable the deployment of image-guidance and enhanced robotic control. This article presents a scene flow algorithm that recovers the deformation and 3D structure of the surgical field-of-view from stereoscopic images by propagating information starting from a sparse set of candidate seed matches. By imposing spatial and temporal constraints the proposed algorithm is able to reconstruct dense 3D scene flow accurately and efficiently. Validation is performed using simulation data to evaluate the method against varying levels of image noise and results are also presented for benchmark phantom model data. The practical value of proposed method is shown by qualitative results for in vivo videos from robotic assisted procedures.
在微创手术期间，关于手术部位组织表面的三维形状和运动的信息对于提供能够实现图像引导和增强机器人控制的度量测量非常重要。本文提出了一种场景流算法，该算法通过从稀疏的候选种子匹配集开始传播信息，从立体图像中恢复手术视野的变形和三维结构。通过施加空间和时间约束，所提出的算法能够准确有效地重建密集的三维场景流。使用模拟数据进行验证，以评估该方法在不同图像噪声水平下的性能，并且还给出了基准体模模型数据的结果。通过机器人辅助手术的体内视频的定性结果展示了所提出方法的实用价值。
