Twin-to-Twin Transfusion Syndrome is commonly treated with minimally invasive laser surgery in fetoscopy. The inter-foetal membrane is used as a reference to find abnormal anastomoses. Membrane identification is a challenging task due to small field of view of the camera, presence of amniotic liquid, foetus movement, illumination changes and noise. This paper aims at providing automatic and fast membrane segmentation in fetoscopic images. We implemented an adversarial network consisting of two Fully-Convolutional Neural Networks. The former (the segmentor) is a segmentation network inspired by U-Net and integrated with residual blocks, whereas the latter acts as critic and is made only of the encoding path of the segmentor. A dataset of 900 images acquired in 6 surgical cases was collected and labelled to validate the proposed approach. The adversarial networks achieved a median Dice similarity coefficient of 91.91% with Inter-Quartile Range (IQR) of 4.63%, overcoming approaches based on U-Net (82.98%-IQR: 14.41%) and U-Net with residual blocks (86.13%-IQR: 13.63%). Results proved that the proposed architecture could be a valuable and robust solution to assist surgeons in providing membrane identification while performing fetoscopic surgery.

中文翻译：

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使用对抗网络进行TTTS的胎儿间膜分割。

双胎双输血综合征通常在胎儿镜检查中采用微创激光手术治疗。胎儿间膜被用作发现异常吻合的参考。由于照相机视野小，羊水的存在，胎儿运动，光照变化和噪声，膜识别是一项具有挑战性的任务。本文旨在为胎儿镜图像提供自动和快速的膜分割。我们实现了一个由两个全卷积神经网络组成的对抗网络。前者（分割器）是受U-Net启发并与残差块集成的分割网络，而后者则充当批注者，仅由分割器的编码路径组成。收集并采集了6个手术病例中900张图像的数据集，并对其进行标记以验证所提出的方法。对抗网络实现了平均Dice相似系数为91.91％，四分位间距（IQR）为4.63％，克服了基于U-Net（82.98％-IQR：14.41％）和基于U-Net的残块（86.13％ -IQR：13.63％）。结果证明，所提出的体系结构可能是有价值的且健壮的解决方案，以协助外科医生在进行窥视镜手术时提供膜鉴定。