The Internet of Things (IoT) technology has been widely introduced to the existing medical system. An eHealth system based on IoT devices has gained widespread popularity. In this article, we propose an IoT eHealth framework to provide an autonomous solution for patients with interventional cardiovascular diseases. In this framework, wearable sensors are used to collect a patient's health data, which is daily monitored by a remote doctor. When the monitoring data is abnormal, the remote doctor will ask for image acquisition of the patient's cardiovascular internal conditions. We leverage edge computing to classify these training images by the local base classifier; thereafter, pseudo-labels are generated according to its output. Moreover, a deep segmentation network is leveraged for the segmentation of stent structs in intravascular optical coherence tomography and intravenous ultrasound images of patients. The experimental results demonstrate that remote and local doctors perform real-time visual communication to complete telesurgery. In the experiments, we adopt the U-net backbone with a pretrained SeResNet34 as the encoder to segment the stent structs. Meanwhile, a series of comparative experiments have been conducted to demonstrate the effectiveness of our method based on accuracy, sensitivity, Jaccard, and dice.

中文翻译：

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基于物联网的支架结构深度分割网络，用于心血管介入诊断

物联网 (IoT) 技术已广泛引入现有医疗系统。基于物联网设备的电子健康系统已广受欢迎。在本文中，我们提出了一个 IoT eHealth 框架，为介入性心血管疾病患者提供自主解决方案。在这个框架中，可穿戴传感器用于收集患者的健康数据，这些数据由远程医生每天监控。当监测数据异常时，远程医生会要求对患者心血管内部情况进行图像采集。我们利用边缘计算通过本地基分类器对这些训练图像进行分类；此后，根据其输出生成伪标签。而且，利用深度分割网络对血管内光学相干断层扫描和患者静脉超声图像中的支架结构进行分割。实验结果表明，远程和本地医生进行实时视觉交流以完成远程手术。在实验中，我们采用带有预训练 SeResNet34 的 U-net 主干作为编码器来分割支架结构。同时，还进行了一系列对比实验，以证明我们基于准确性、灵敏度、Jaccard 和骰子的方法的有效性。我们采用带有预训练 SeResNet34 的 U-net 主干作为编码器来分割支架结构。同时，还进行了一系列对比实验，以证明我们基于准确性、灵敏度、Jaccard 和骰子的方法的有效性。我们采用带有预训练 SeResNet34 的 U-net 主干作为编码器来分割支架结构。同时，还进行了一系列对比实验，以证明我们基于准确性、灵敏度、Jaccard 和骰子的方法的有效性。