Twin-to-Twin Transfusion Syndrome (TTTS) is a rare condition that affects about 15% of monochorionic pregnancies, in which identical twins share a single placenta. Fetoscopic laser photocoagulation (FLP) is the standard treatment for TTTS, which significantly improves the survival of fetuses. The aim of FLP is to identify abnormal connections between blood vessels and to laser ablate them in order to equalize blood supply to both fetuses. However, performing fetoscopic surgery is challenging due to limited visibility, a narrow field of view, and significant variability among patients and domains. In order to enhance the visualization of placental vessels during surgery, we propose TTTSNet, a network architecture designed for real-time and accurate placental vessel segmentation. Our network architecture incorporates a novel channel attention module and multi-scale feature fusion module to precisely segment tiny placental vessels. To address the challenges posed by FLP-specific fiberscope and amniotic sac-based artifacts, we employed novel data augmentation techniques. These techniques simulate various artifacts, including laser pointer, amniotic sac particles, and structural and optical fiber artifacts. By incorporating these simulated artifacts during training, our network architecture demonstrated robust generalizability. We trained TTTSNet on a publicly available dataset of 2060 video frames from 18 independent fetoscopic procedures and evaluated it on a multi-center external dataset of 24 in-vivo procedures with a total of 2348 video frames. Our method achieved significant performance improvements compared to state-of-the-art methods, with a mean Intersection over Union of 78.26% for all placental vessels and 73.35% for a subset of tiny placental vessels. Moreover, our method achieved 172 and 152 frames per second on an A100 GPU, and Clara AGX, respectively. This potentially opens the door to real-time application during surgical procedures. The code is publicly available at https://github.com/SanoScience/TTTSNet.

中文翻译：

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胎儿镜激光手术中 Twin-to-Twin 输血综合征的实时胎盘血管分割


双胞胎对双胞胎输血综合征 （TTTS） 是一种罕见的疾病，影响约 15% 的单绒毛膜妊娠，其中同卵双胞胎共享一个胎盘。胎镜激光光凝术 （FLP） 是 TTTS 的标准治疗方法，可显着提高胎儿的存活率。FLP 的目的是识别血管之间的异常连接并对其进行激光消融，以平衡两个胎儿的血液供应。然而，由于能见度有限、视野狭窄以及患者和领域之间的显着差异，进行胎儿镜手术具有挑战性。为了增强手术过程中胎盘血管的可视化，我们提出了 TTTSNet，这是一种专为实时和准确的胎盘血管分割而设计的网络架构。我们的网络架构结合了新颖的通道注意力模块和多尺度特征融合模块，以精确分割微小的胎盘血管。为了解决 FLP 特异性纤维镜和基于羊膜囊的伪影带来的挑战，我们采用了新颖的数据增强技术。这些技术模拟各种伪影，包括激光指示器、羊膜囊颗粒以及结构和光纤伪影。通过在训练期间整合这些模拟工件，我们的网络架构展示了强大的泛化性。我们在来自 18 个独立胎儿镜程序的 2060 个视频帧的公开数据集上训练了 TTTSNet，并在 24 个体内程序的多中心外部数据集上对其进行了评估，总共 2348 个视频帧。与最先进的方法相比，我们的方法实现了显着的性能改进，所有胎盘血管的平均交集比为 78.26%，微小胎盘血管子集的平均交集比为 73.35%。 此外，我们的方法在 A100 GPU 和 Clara AGX 上分别实现了每秒 172 帧和 152 帧。这可能会为外科手术过程中的实时应用打开大门。该代码在 https://github.com/SanoScience/TTTSNet 上公开提供。