EnCoV, Institut Pascal, UMR 6602 CNRS/Université Clermont-Auvergne, Clermont-Ferrand, France.
Faculté de Médecine, Batiment 3C, 28 place Henri Dunant, 63001, Clermont-Ferrand, France.
Ann Biomed Eng. 2020 Jun;48(6):1712-1727. doi: 10.1007/s10439-020-02479-z. Epub 2020 Feb 28.
Augmented Reality (AR) in monocular liver laparoscopy requires one to register a preoperative 3D liver model to a laparoscopy image. This is a difficult problem because the preoperative shape may significantly differ from the unknown intraoperative shape and the liver is only partially visible in the laparoscopy image. Previous approaches are either manual, using a rigid model, or automatic, using visual cues and a biomechanical model. We propose a new approach called the hybrid approach combining the best of both worlds. The visual cues allow us to capture the machine perception while user interaction allows us to take advantage of the surgeon's prior knowledge and spatial understanding of the patient anatomy. The registration accuracy and repeatability were evaluated on phantom, animal ex vivo and patient data respectively. The proposed registration outperforms the state of the art methods both in terms of accuracy and repeatability. An average registration error below the 1 cm oncologic margin advised in the literature for tumour resection in laparoscopy hepatectomy was obtained.
单目腹腔镜中的增强现实 (AR) 需要将术前的 3D 肝脏模型注册到腹腔镜图像中。这是一个难题，因为术前形状可能与未知的术中形状有很大的不同，而且肝脏在腹腔镜图像中只能部分可见。以前的方法要么是手动的，使用刚性模型，要么是自动的，使用视觉线索和生物力学模型。我们提出了一种称为混合方法的新方法，它结合了两者的优点。视觉线索使我们能够捕捉机器感知，而用户交互使我们能够利用外科医生对患者解剖结构的先验知识和空间理解。分别在体模、动物离体和患者数据上评估了配准的准确性和可重复性。所提出的注册方法在准确性和可重复性方面都优于最新方法。在腹腔镜肝切除术中肿瘤切除的文献中建议的平均小于 1 厘米的肿瘤学边界的平均注册误差。
