PURPOSE Twin-to-twin transfusion syndrome (TTTS) is a placental defect occurring in monochorionic twin pregnancies. It is associated with high risks of fetal loss and perinatal death. Fetoscopic elective laser ablation (ELA) of placental anastomoses has been established as the most effective therapy for TTTS. Current tools and techniques face limitations in case of more complex ELA cases. Visualization of the entire placental surface and vascular equator; maintaining an adequate distance and a close to perpendicular angle between laser fiber and placental surface are central for the effectiveness of laser ablation and procedural success. Robot-assisted technology could address these challenges, offer enhanced dexterity and ultimately improve the safety and effectiveness of the therapeutic procedures. METHODS This work proposes a 'minimal' robotic TTTS approach whereby rather than deploying a massive and expensive robotic system, a compact instrument is 'robotised' and endowed with 'robotic' skills so that operators can quickly and efficiently use it. The work reports on automatic placental pose estimation in fetoscopic images. This estimator forms a key building block of a proposed shared-control approach for semi-autonomous fetoscopy. A convolutional neural network (CNN) is trained to predict the relative orientation of the placental surface from a single monocular fetoscope camera image. To overcome the absence of real-life ground-truth placenta pose data, similar to other works in literature (Handa et al. in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016; Gaidon et al. in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016; Vercauteren et al. in: Proceedings of the IEEE, 2019) the network is trained with data generated in a simulated environment and an in-silico phantom model. A limited set of coarsely manually labeled samples from real interventions are added to the training dataset to improve domain adaptation. RESULTS The trained network shows promising results on unseen samples from synthetic, phantom and in vivo patient data. The performance of the network for collaborative control purposes was evaluated in a virtual reality simulator in which the virtual flexible distal tip was autonomously controlled by the neural network. CONCLUSION Improved alignment was established compared to manual operation for this setting, demonstrating the feasibility to incorporate a CNN-based estimator in a real-time shared control scheme for fetoscopic applications.

中文翻译：

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基于深度学习的单眼胎盘姿势估计：在胎儿镜检查中实现协作机器人。

目的 双胎输血综合征 (TTTS) 是一种胎盘缺陷，发生在单绒毛膜双胎妊娠中。它与胎儿丢失和围产期死亡的高风险有关。胎盘吻合术的胎儿镜选择性激光消融 (ELA) 已被确定为 TTTS 最有效的治疗方法。在更复杂的 ELA 案例中，当前的工具和技术面临限制。整个胎盘表面和血管赤道的可视化；在激光纤维和胎盘表面之间保持足够的距离和接近垂直的角度是激光消融的有效性和手术成功的核心。机器人辅助技术可以解决这些挑战，提高灵活性并最终提高治疗程序的安全性和有效性。方法 这项工作提出了一个“最小的” 机器人 TTTS 方法，而不是部署庞大且昂贵的机器人系统，而是将紧凑型仪器“机器人化”并赋予“机器人”技能，以便操作员可以快速有效地使用它。该工作报告了胎儿镜图像中自动胎盘姿态估计。该估计器构成了拟议的半自主胎儿镜检查共享控制方法的关键组成部分。训练卷积神经网络 (CNN) 以从单个单目胎儿镜相机图像预测胎盘表面的相对方向。为了克服现实生活中胎盘姿势数据的缺失，类似于文献中的其他作品（Handa 等人在：IEEE 计算机视觉和模式识别会议论文集，2016 年；Gaidon 等人在：IEEE 计算机视觉与模式识别会议论文集，2016；Vercauteren 等人。in: Proceedings of the IEEE, 2019) 该网络使用在模拟环境中生成的数据和 in-silico 幻影模型进行训练。将来自真实干预的一组有限的粗略手动标记的样本添加到训练数据集中，以提高域适应能力。结果经过训练的网络在来自合成、幻像和体内患者数据的未见样本上显示出有希望的结果。用于协作控制目的的网络性能在虚拟现实模拟器中进行评估，其中虚拟柔性远端尖端由神经网络自主控制。结论与此设置的手动操作相比，建立了改进的对齐方式，