PURPOSE 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. METHODS 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. RESULTS 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. CONCLUSIONS 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.

中文翻译：

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检测子宫的闭合轮廓以自动执行增强腹腔镜检查：评分，丢失，数据集，评估和用户研究。

目的术前3D模型的注册，例如从MRI重建到术中腹腔镜2D图像，是实现腹腔镜增强现实的主要挑战。当前的系统有一个主要的局限性：它们要求外科医生在手术过程中手动标记阻塞轮廓。这就要求外科医生充分理解咬合轮廓和外科医生时间的非平凡概念，直接影响验收和可用性。为了克服此限制，我们提出了一个用于对象类遮挡轮廓检测（OC2D）的完整框架，并将其应用于子宫手术。方法我们的第一个贡献是符合所有相关性能标准的新的基于距离的评估得分。我们的第二个贡献是将交叉熵和两个旨在提高1像素厚度响应的新惩罚相结合的损失函数。这使我们能够端到端地训练U-Net，胜过所有竞争方法，这往往会产生较厚的响应。我们的第三个贡献是一个3818个经过精心标记的子宫腹腔镜检查图像的数据集，该数据集用于训练和评估我们的检测器。结果评估表明，所提出的检测器具有与现有方法相似的错误假阴性率，但是大大降低了错误阳性率和响应厚度。最后，我们进行了一项用户研究，以评估OC2D对增强腹腔镜中手动标记的咬合轮廓的影响。我们使用了10例记录的妇科腹腔镜检查法，涉及5名外科医生。使用OC2D可以减少3分钟和53 s的外科医生时间，而不会影响套准精度。结论我们提供了一套新的标准和基于距离的量度来评估OC2D方法。我们提出了一种OC2D方法，该方法优于最新方法。从用户研究中获得的结果表明，全自动增强腹腔镜检查是可行的。