Although robot-assisted cardiovascular catheterization is commonly performed for intervention of cardiovascular diseases, more studies are needed to support the procedure with automated tool segmentation. This can aid surgeons on tool tracking and visualization during intervention. Learning-based segmentation has recently offered state-of-the-art segmentation performances however, generating ground-truth signals for fully-supervised methods is labor-intensive and time consuming for the interventionists. In this study, a weakly-supervised learning method with multi-lateral pseudo labeling is proposed for tool segmentation in cardiac angiograms. The method includes a modified U-Net model with one encoder and multiple lateral-branched decoders that produce pseudo labels as supervision signals under different perturbation. The pseudo labels are self-generated through a mixed loss function and shared consistency in the decoders. We trained the model end-to-end with weakly-annotated data obtained during robotic cardiac catheterization. Experiments with the proposed model shows weakly annotated data has closer performance to when fully annotated data is used. Compared to three existing weakly-supervised methods, our approach yielded higher segmentation performance across three different cardiac angiogram data. With ablation study, we showed consistent performance under different parameters. Thus, we offer a less expensive method for real-time tool segmentation and tracking during robot-assisted cardiac catheterization.

翻译：尽管机器人辅助心血管导管插入术常用于心血管疾病的介入治疗，但仍需更多研究通过自动化工具分割来支持该手术过程，这有助于外科医生在介入过程中进行工具追踪和可视化。基于学习的分割方法最近展现了最先进的性能，然而，为全监督方法生成真实标注信号对介入医生而言既耗时又费力。本研究提出了一种基于多侧伪标签的弱监督学习方法，用于心血管造影中的工具分割。该方法包含一个改进的U-Net模型，该模型由一个编码器和多个侧分支解码器组成，在不同扰动下生成伪标签作为监督信号。这些伪标签通过混合损失函数和解码器间的共享一致性自生成。我们使用机器人心脏导管插入术中获取的弱标注数据对模型进行端到端训练。实验表明，使用弱标注数据时的性能接近使用全标注数据的效果。与三种现有弱监督方法相比，我们的方法在三个不同心血管造影数据集上均获得了更高的分割性能。消融实验显示，该方法在不同参数下表现稳定。因此，我们为机器人辅助心脏导管插入术中的实时工具分割与追踪提供了一种成本更低的方法。