Université Clermont Auvergne, CHU Clermont-Ferrand, CNRS, SIGMA, Institut Pascal, Clermont-Ferrand, France.
Be-Studys, A Brand of Be-Ys Group, 123 Route de Meyrin, 1219 Châtelaine, Suisse, Vernier, Switzerland.
Int J Comput Assist Radiol Surg. 2020 Jul;15(7):1177-1186. doi: 10.1007/s11548-020-02151-w. Epub 2020 May 5.
The registration of a preoperative 3D model, reconstructed, for example, from MRI, to intraoperative laparoscopy 2D images, is the main challenge to achieve augmented reality in laparoscopy. The current systems have a major limitation: they require that the surgeon manually marks the occluding contours during surgery. This requires the surgeon to fully comprehend the non-trivial concept of occluding contours and surgeon time, directly impacting acceptance and usability. To overcome this limitation, we propose a complete framework for object-class occluding contour detection (OC2D), with application to uterus surgery.
Our first contribution is a new distance-based evaluation score complying with all the relevant performance criteria. Our second contribution is a loss function combining cross-entropy and two new penalties designed to boost 1-pixel thickness responses. This allows us to train a U-Net end to end, outperforming all competing methods, which tends to produce thick responses. Our third contribution is a dataset of 3818 carefully labelled laparoscopy images of the uterus, which was used to train and evaluate our detector.
Evaluation shows that the proposed detector has a similar false false-negative rate to existing methods but substantially reduces both false-positive rate and response thickness. Finally, we ran a user study to evaluate the impact of OC2D against manually marked occluding contours in augmented laparoscopy. We used 10 recorded gynecologic laparoscopies and involved 5 surgeons. Using OC2D led to a reduction of 3 min and 53 s in surgeon time without sacrificing registration accuracy.
We provide a new set of criteria and a distance-based measure to evaluate an OC2D method. We propose an OC2D method which outperforms the state-of-the-art methods. The results obtained from the user study indicate that fully automatic augmented laparoscopy is feasible.
将术前重建的 3D 模型（例如，从 MRI 重建）注册到术中腹腔镜 2D 图像，是实现腹腔镜增强现实的主要挑战。当前的系统存在一个主要限制：它们要求外科医生在手术过程中手动标记遮挡轮廓。这需要外科医生完全理解遮挡轮廓的非平凡概念和外科医生的时间，这直接影响接受度和可用性。为了克服这一限制，我们提出了一个完整的对象类遮挡轮廓检测（OC2D）框架，应用于子宫手术。
我们的第一个贡献是一个新的基于距离的评估分数，符合所有相关的性能标准。我们的第二个贡献是一个损失函数，结合了交叉熵和两个新的惩罚项，旨在增强 1 像素厚度的响应。这使我们能够端到端地训练一个 U-Net，从而超越所有竞争方法，这些方法往往会产生较厚的响应。我们的第三个贡献是一个包含 3818 张精心标记的子宫腹腔镜图像的数据集，用于训练和评估我们的检测器。
评估表明，所提出的检测器具有与现有方法相似的假阴性率，但大大降低了假阳性率和响应厚度。最后，我们进行了一项用户研究，以评估在增强腹腔镜中使用 OC2D 与手动标记遮挡轮廓的影响。我们使用了 10 个记录的妇科腹腔镜，并涉及了 5 名外科医生。使用 OC2D 可以减少 3 分钟 53 秒的手术时间，而不会牺牲注册准确性。
我们提供了一套新的标准和基于距离的度量来评估 OC2D 方法。我们提出了一种 OC2D 方法，该方法优于最先进的方法。用户研究的结果表明，完全自动增强腹腔镜是可行的。
