Yale University School of Medicine, New Haven, USA.
Department of Obstetrics and Gynecology, Yale University School of Medicine, New Haven, USA.
Int J Comput Assist Radiol Surg. 2019 Feb;14(2):227-235. doi: 10.1007/s11548-018-1886-4. Epub 2018 Nov 27.
Twin-to-twin transfusion syndrome (TTTS) is a potentially lethal condition that affects pregnancies in which twins share a single placenta. The definitive treatment for TTTS is fetoscopic laser photocoagulation, a procedure in which placental blood vessels are selectively cauterized. Challenges in this procedure include difficulty in quickly identifying placental blood vessels due to the many artifacts in the endoscopic video that the surgeon uses for navigation. We propose using deep-learned segmentations of blood vessels to create masks that can be recombined with the original fetoscopic video frame in such a way that the location of placental blood vessels is discernable at a glance.
In a process approved by an institutional review board, intraoperative videos were acquired from ten fetoscopic laser photocoagulation surgeries performed at Yale New Haven Hospital. A total of 345 video frames were selected from these videos at regularly spaced time intervals. The video frames were segmented once by an expert human rater (a clinician) and once by a novice, but trained human rater (an undergraduate student). The segmentations were used to train a fully convolutional neural network of 25 layers.
The neural network was able to produce segmentations with a high similarity to ground truth segmentations produced by an expert human rater (sensitivity = 92.15% ± 10.69%) and produced segmentations that were significantly more accurate than those produced by a novice human rater (sensitivity = 56.87% ± 21.64%; p < 0.01).
A convolutional neural network can be trained to segment placental blood vessels with near-human accuracy and can exceed the accuracy of novice human raters. Recombining these segmentations with the original fetoscopic video frames can produced enhanced frames in which blood vessels are easily detectable. This has significant implications for aiding fetoscopic surgeons-especially trainees who are not yet at an expert level.
双胎输血综合征（TTTS）是一种潜在致命的疾病，影响到共享同一胎盘的双胞胎妊娠。TTTS 的明确治疗方法是胎儿镜激光凝固术，这是一种选择性烧灼胎盘血管的程序。该手术的挑战包括由于外科医生用于导航的内窥镜视频中存在许多伪影，因此很难快速识别胎盘血管。我们建议使用血管的深度学习分割来创建蒙版，然后可以将蒙版与原始胎儿镜视频帧重新组合，以便一眼就能识别胎盘血管的位置。
在机构审查委员会批准的过程中，从耶鲁纽黑文医院进行的十次胎儿镜激光凝固手术中获取术中视频。从这些视频中以固定的时间间隔选择了总共 345 个视频帧。这些视频帧由一位专家人类评分者（临床医生）和一位经验不足但受过训练的人类评分者（本科生）分别进行了一次分割。分割结果用于训练一个由 25 层组成的全卷积神经网络。
神经网络能够生成与专家人类评分者生成的真实分割高度相似的分割（敏感性=92.15%±10.69%），并且生成的分割明显比经验不足的人类评分者更准确（敏感性=56.87%±21.64%；p<0.01）。
可以训练卷积神经网络以接近人类的准确性分割胎盘血管，并且可以超过经验不足的人类评分者的准确性。将这些分割与原始胎儿镜视频帧重新组合可以生成增强的帧，其中血管很容易检测到。这对辅助胎儿镜外科医生（尤其是尚未达到专家水平的受训者）具有重要意义。
