使用卷积神经网络进行静脉细分和上下四肢的可视化
Amit Laddi1,2, Shivalika Goyal1,2, Himani
1Biomedical Applications Group, CSIR-Central Scientific Instruments Organisation (CSIO), Chandigarh-160030, India.
Biomedizinische Technik. Biomedical engineering
|April 23, 2024
概括
一个新的深度学习算法,自我参数化的U-Net,准确地实时可视化静脉. 这项技术有助于血管外科医生进行诸如静脉穿刺和慢性静脉疾病治疗等手术.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 血管外科 血管外科
背景情况:
- 精确的静脉定位对于有效的静脉穿刺,血管手术和慢性静脉疾病 (CVD) 管理至关重要.
- 目前用于静脉可视化的方法可能具有挑战性,特别是在不受约束的条件下.
研究的目的:
- 开发一种可靠的实时框架,用于静脉定位,识别和可视化,使用深度学习 (DL) 自参数化的卷积神经网络 (CNN).
- 从使用近红外 (NIR) 成像在不受约束的条件下获得的下肢和上肢数据集进行静脉地图细分.
- 协助血管外科医生进行诸如静脉穿刺,血管外科手术和心血管疾病治疗等手术.
主要方法:
- 使用便携式图像采集装置,从72名受试者中收集静脉数据.
- 一个手动注释的数据集训练并比较了传统的CNN架构 (ResNet,VGGNet) 与一个自我参数化的U-Net.
- 重点是改进自动静脉细分和可视化.
主要成果:
- 与传统的CNN模型相比,自参数化的U-Net在分割不受约束的数据集方面表现出卓越的性能.
- 在实时静脉可视化方面获得0.58的子得分和96.7%的准确性.
- 该模型在不受约束的条件下对实时静脉位置有效.
结论:
- 自参数化的U-Net显示了静脉细分和可视化的巨大潜力.
- 它可能会降低与传统静脉穿刺和心血管疾病治疗相关的风险.
- 这种先进的CNN架构提供了改进的血管辅助,提高了患者护理和治疗结果.
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