胎儿镜检查激光光凝术是一种广泛采用的治疗双双输血综合征(TTTS)的方法。该程序涉及光凝病理性吻合,以恢复双胞胎之间的生理血液交换。该程序特别具有挑战性,从外科医生的角度来看,由于视野有限,胎儿镜的可操作性差,由于羊水浑浊,能见度差,和光照的可变性。这些挑战可能导致手术时间增加和病理性吻合的不完全消融,导致持续的TTTS。计算机辅助干预(CAI)可以通过识别场景中的关键结构并通过视频马赛克扩展胎儿镜视野,为TTTS外科医生提供决策支持和情境意识。由于缺乏高质量的设计数据,这一领域的研究受到了阻碍,开发和测试CAI算法。通过胎儿镜胎盘血管分割和注册(FetReg2021)挑战,这是作为MICCAI2021内窥镜视觉(EndoVis)挑战的一部分组织的,我们发布了第一个大规模多中心TTTS数据集,用于开发广义和稳健的语义分割和视频马赛克算法,重点是从长持续时间的胎儿镜检查视频中创建无漂移马赛克.对于这个挑战,我们发布了2060张图片的数据集,像素注释的血管,工具,胎儿和背景类,来自18个体内TTTS胎儿镜检查程序和18个平均长度为411帧的短视频剪辑,用于开发胎盘场景分割和用于镶嵌技术的帧配准。七个团队参加了这项挑战,他们的模型性能在一个看不见的测试数据集上进行了评估,该数据集包含来自6个胎儿镜程序和6个短片的658个像素注释图像。对于分段任务,执行的总体基线是表现最好的(总计mIoU为0.6763),在血管级(mIoU为0.5817)中表现最好,而在工具级(mIoU为0.6335)和胎儿级(mIoU为0.5178)中团队RREB表现最好.对于注册任务,总体而言,基线表现优于SANO团队,整体平均5帧SSIM为0.9348.定性,观察到SANO团队在平面场景中表现更好,而基线在非计划者方案中更好。详细的分析表明,没有一个团队在所有6个测试胎儿镜视频中表现出色。挑战提供了一个机会,可以创建通用的解决方案来理解和镶嵌场景。在本文中,我们介绍了FetReg2021挑战的结果,同时报告了TTTS胎儿镜检查中CAI的详细文献综述。通过这次挑战,它的分析和多中心胎儿数据的发布,我们为该领域未来的研究提供了一个基准。
Fetoscopy laser photocoagulation is a widely adopted procedure for treating Twin-to-Twin Transfusion Syndrome (TTTS). The procedure involves photocoagulation pathological anastomoses to restore a physiological blood exchange among twins. The procedure is particularly challenging, from the surgeon\'s side, due to the limited field of view, poor manoeuvrability of the fetoscope, poor visibility due to amniotic fluid turbidity, and variability in illumination. These challenges may lead to increased surgery time and incomplete ablation of pathological anastomoses, resulting in persistent TTTS. Computer-assisted intervention (CAI) can provide TTTS surgeons with decision support and context awareness by identifying key structures in the scene and expanding the fetoscopic field of view through video mosaicking. Research in this domain has been hampered by the lack of high-quality data to design, develop and test CAI algorithms. Through the Fetoscopic Placental Vessel Segmentation and Registration (FetReg2021) challenge, which was organized as part of the MICCAI2021 Endoscopic Vision (EndoVis) challenge, we released the first large-scale multi-center TTTS dataset for the development of generalized and robust semantic segmentation and video mosaicking algorithms with a focus on creating drift-free mosaics from long duration
fetoscopy videos. For this challenge, we released a dataset of 2060 images, pixel-annotated for vessels, tool, fetus and background classes, from 18 in-vivo TTTS
fetoscopy procedures and 18 short video clips of an average length of 411 frames for developing placental scene segmentation and frame registration for mosaicking techniques. Seven teams participated in this challenge and their model performance was assessed on an unseen test dataset of 658 pixel-annotated images from 6 fetoscopic procedures and 6 short clips. For the segmentation task, overall baseline performed was the top performing (aggregated mIoU of 0.6763) and was the best on the vessel class (mIoU of 0.5817) while team RREB was the best on the tool (mIoU of 0.6335) and fetus (mIoU of 0.5178) classes. For the registration task, overall the baseline performed better than team SANO with an overall mean 5-frame SSIM of 0.9348. Qualitatively, it was observed that team SANO performed better in planar scenarios, while baseline was better in non-planner scenarios. The detailed analysis showed that no single team outperformed on all 6 test fetoscopic videos. The challenge provided an opportunity to create generalized solutions for fetoscopic scene understanding and mosaicking. In this paper, we present the findings of the FetReg2021 challenge, alongside reporting a detailed literature review for CAI in TTTS
fetoscopy. Through this challenge, its analysis and the release of multi-center fetoscopic data, we provide a benchmark for future research in this field.