Stoyanov Danail, Scarzanella Marco Visentini, Pratt Philip, Yang Guang-Zhong
Institute of Biomedical Engineering, Imperial College London, London SW7 2AZ, UK.
Med Image Comput Comput Assist Interv. 2010;13(Pt 1):275-82. doi: 10.1007/978-3-642-15705-9_34.
The recovery of 3D tissue structure and morphology during robotic assisted surgery is an important step towards accurate deployment of surgical guidance and control techniques in minimally invasive therapies. In this article, we present a novel stereo reconstruction algorithm that propagates disparity information around a set of candidate feature matches. This has the advantage of avoiding problems with specular highlights, occlusions from instruments and view dependent illumination bias. Furthermore, the algorithm can be used with any feature matching strategy allowing the propagation of depth in very disparate views. Validation is provided for a phantom model with known geometry and this data is available online in order to establish a structured validation scheme in the field. The practical value of the proposed method is further demonstrated by reconstructions on various in vivo images of robotic assisted procedures, which are also available to the community.
在机器人辅助手术中恢复三维组织结构和形态是在微创治疗中准确部署手术引导和控制技术的重要一步。在本文中，我们提出了一种新颖的立体重建算法，该算法围绕一组候选特征匹配传播视差信息。这具有避免镜面高光、器械遮挡和视图相关照明偏差问题的优点。此外，该算法可与任何特征匹配策略一起使用，从而允许在非常不同的视图中传播深度信息。针对具有已知几何形状的体模模型进行了验证，并且该数据可在线获取，以便在该领域建立结构化验证方案。通过对机器人辅助手术的各种体内图像进行重建，进一步证明了所提出方法的实际价值，这些图像也可供社区使用。
