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 .

中文翻译：

---

双胎输血综合征胎儿镜激光手术中的实时胎盘血管分割


双胎输血综合症 (TTTS) 是一种罕见疾病，影响约 15% 的单绒毛膜妊娠，即同卵双胞胎共享同一个胎盘。胎儿镜激光光凝术 (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，并用激光消融它们，以平衡两个胎儿的血液供应。然而，由于视野有限、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，一种专为实时准确的胎盘血管分割而设计的网络架构。我们的网络架构采用了新颖的通道注意模块和多尺度特征融合模块来精确分割微小的胎盘血管。为了解决 FLP 专用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，包括激光笔、羊膜囊颗粒以及结构和光纤伪影。通过在训练期间合并这些模拟工件，我们的网络架构表现出了强大的通用性。我们在来自 18 个独立胎儿镜手术的 2060 个视频帧的公开数据集上训练 TTTSNet，并在包含 24 个体内手术、总共 2348 个视频帧的多中心外部数据集上对其进行评估。与最先进的方法相比，我们的方法取得了显着的性能改进，所有胎盘血管的平均交集率为 78.26%，部分微小胎盘血管的平均交集率为 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。这可能为外科手术期间的实时应用打开了大门。该代码可在 公开获取。