EnCoV, Institut Pascal, UMR 6602, CNRS/UBP/SIGMA, 63000, Clermont-Ferrand, France.
IRCAD and IHU-Strasbourg, 1 Place de l'Hopital, 67000, Strasbourg, France.
Int J Comput Assist Radiol Surg. 2019 Jul;14(7):1237-1245. doi: 10.1007/s11548-019-02001-4. Epub 2019 May 30.
The registration of preoperative 3D images to intra-operative laparoscopic 2D images is one of the main concerns for augmented reality in computer-assisted surgery. For laparoscopic liver surgery, while several algorithms have been proposed, there is neither a public dataset nor a systematic evaluation methodology to quantitatively evaluate registration accuracy.
Our main contribution is to provide such a dataset with an in vivo porcine model. It is used to evaluate a state-of-the-art registration algorithm that is capable of simultaneous registration and soft-body collision reasoning.
The dataset consists of 13 deformed liver states, with corresponding exploration videos and interventional CT acquisitions with 60 small artificial fiducials located on the surface of the liver and distributed within the parenchyma, where a precise registration is crucial for augmented reality. This dataset will be made public. Using this dataset, we show that collision reasoning improves performance of registration for strong deformation and independent lobe motion.
This dataset addresses the lack of public datasets in this field. As an example of use, we present and evaluate a state-of-the-art energy-based approach and a novel extension that handles self-collisions.
将术前的 3D 图像注册到术中的腹腔镜 2D 图像是计算机辅助手术中增强现实的主要关注点之一。对于腹腔镜肝手术，虽然已经提出了几种算法，但既没有公共数据集，也没有系统的评估方法来定量评估注册准确性。
我们的主要贡献是提供这样一个数据集，使用活体猪模型。它用于评估一种先进的注册算法，该算法能够同时进行注册和软体力碰撞推理。
该数据集包含 13 个变形的肝脏状态，以及相应的探索视频和介入 CT 采集，在肝脏表面有 60 个小的人工基准点，分布在肝实质内，对于增强现实来说，精确的注册至关重要。该数据集将公开。使用这个数据集，我们表明碰撞推理可以提高对强变形和独立叶运动的注册性能。
该数据集解决了该领域缺乏公共数据集的问题。作为使用示例，我们提出并评估了一种基于能量的最新方法和一种处理自碰撞的新扩展。
