A DEEP SEGMENTATION NETWORK OF STENT STRUCTS BASED ON IOT FOR INTERVENTIONAL CARDIOVASCULAR DIAGNOSIS

A DEEP SEGMENTATION NETWORK OF STENT STRUCTS BASED ON IOT FOR INTERVENTIONAL CARDIOVASCULAR DIAGNOSIS
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DOI:
10.1109/mwc.001.2000407
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发表时间:
2021-06-01
影响因子:
12.9
通讯作者:
Mukherjee, Mithun
Mukherjee, Mithun
中科院分区:
计算机科学1区
文献类型:
--
作者:
Huang, Chenxi;Zong, Yongshuo;Mukherjee, Mithun

文献摘要

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物联网(IoT)技术已被广泛引入现有的医疗系统。基于物联网设备的电子健康系统已经得到了广泛的普及。在本文中,我们提出了一个物联网电子健康框架,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,远程医生会要求获取患者心血管内部情况的图像。我们利用边缘计算的局部基分类器对这些训练图像进行分类,然后根据其输出生成伪标签。此外,利用深度分割网络对患者的血管内光学相干断层扫描和静脉内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通信,以完成手术。在实验中,我们采用了U-网骨干与预先训练的SeResNet 34作为编码器分割支架结构。同时,一系列的对比实验已经进行了证明我们的方法的有效性的基础上准确性,灵敏度,Jaccard,骰子。
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.