Sano Centre for Computational Medicine, Cracow, Poland; Informatics Institute, University of Amsterdam, Amsterdam, The Netherlands; Department of Biomedical Engineering and Physics, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands.
Sano Centre for Computational Medicine, Cracow, Poland.
Med Image Anal. 2025 Jan;99:103330. doi: 10.1016/j.media.2024.103330. Epub 2024 Aug 30.
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.
双胎输血综合征 (TTTS) 是一种罕见的疾病，影响约 15%的单绒毛膜妊娠，其中同卵双胞胎共用一个胎盘。胎儿镜激光凝固术 (FLP) 是 TTTS 的标准治疗方法，显著提高了胎儿的存活率。FLP 的目的是识别血管之间的异常连接，并通过激光消融它们来使两个胎儿的血液供应均等。然而，由于可视性有限、视场狭窄以及患者和领域之间存在显著差异，进行胎儿镜手术具有挑战性。为了在手术中增强胎盘血管的可视化，我们提出了 TTTSNet，这是一种专为实时准确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 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。这些技术模拟了各种伪影，包括激光笔、羊膜囊颗粒以及结构和光纤伪影。通过在训练过程中加入这些模拟的伪影，我们的网络架构表现出强大的泛化能力。我们在一个包含 18 个独立胎儿镜手术的 2060 个视频帧的公共数据集上训练 TTTSNet，并在一个包含 2348 个视频帧的多中心外部数据集上对其进行评估，该数据集包含 24 个体内手术。与最先进的方法相比，我们的方法取得了显著的性能提升，所有胎盘血管的平均交并率为 78.26%，微小胎盘血管子集的平均交并率为 73.35%。此外，我们的方法在 A100 GPU 和 Clara AGX 上的帧率分别达到了 172 帧/秒和 152 帧/秒。这可能为手术过程中的实时应用开辟了道路。代码可在 https://github.com/SanoScience/TTTSNet 上获取。
