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一个近红外成像系统用于机器人静脉血液采集

Zhikang Yang¹, Mao Shi¹, Yassine Gharbi¹

¹Laboratory of Locomotion Bioinspiration and Intelligent Robots, College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.

Sensors (Basel, Switzerland)

|November 27, 2024

概括

这项研究引入了一种新的静脉成像系统和U-Net+ResNet18神经网络,用于机器人血液采集中增强静脉细分. 这种新方法显著提高了图像细分的准确性和立体声匹配性能.

科学领域:

医疗机器人医疗机器人
医疗成像医学成像
计算机视觉计算机视觉

背景情况:

机器人静脉血液采集提供了潜在的手动方法.
高质量的静脉成像对于成功的机器人血液采集至关重要.
精确的静脉细分对于精确的机器人导航至关重要.

研究的目的:

开发一个先进的静脉成像设备和一个强大的静脉细分算法.
提高用于机器人应用的静脉成像的准确性和可靠性.
通过深度学习和优化立体相匹配来提高静脉图像细分的性能.

主要方法:

开发了一种基于对成像参数的模拟分析的静脉成像设备.
提出了一个U-Net+ResNet18神经网络,集成ResNet18残余块用于静脉细分.
利用Bootstrap Your Own Latent (BYOL) 预培训用于ResNet18编码器,转移参数以增强有限数据的细分.
优化了AD-Census的立体相匹配算法,使用可变重量方法来提高适应性.

主要成果:

通过BYOL+U-Net+ResNet18方法,比标准U-Net显著改进,包括二进制交叉度 (BCE) 减少8.31%,交叉度 (IoU) 增加15.95%.
豪斯多夫距离 (HD) 减少了5.50%,子系数 (Dice) 增加了9.20%,表明分段质量优越.
优化的AD-Census立体相匹配算法实现了25.69%的平均误差降低,显示了增强的立体相匹配性能.

结论:

开发的静脉成像系统和U-Net+ResNet18网络显著提高了静脉图像细分的准确性.
优化的立体声匹配提高了静脉成像系统的可靠性.
该系统在未来的机器人静脉血液采集应用中显示出实时穿孔指导的前景.

关键词:

在U-Net+ResNet18神经网络中.接近红外线的静脉成像系统.静脉图像细分静脉图像细分静脉血液采集静脉血液采集

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