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一种用于子语言图像分割和色彩分析的自动方法.

Zhecheng Yang, Hongyu Gu, Hong Chen

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 12, 2023
    PubMed
    概括

    本研究引入了先进的计算机视觉技术,用于精确的语言下图像细分和静脉颜色分析,提高了传统中医的诊断准确性. 这些新方法提高了对临床医生和患者的客观疾病评估.

    科学领域:

    • 计算机视觉 计算机视觉
    • 医疗成像医学成像
    • 传统中国医学 诊断 诊断 诊断

    背景情况:

    • 准确的语言下图像分析对于传统中医 (TCM) 疾病诊断至关重要.
    • 目前对舌下静脉的细分和色彩分析方法缺乏精度,并且受到观察者之间的变化.
    • 自动化处理为舌头观察提供了一种非侵入性的,方便的方法.

    研究的目的:

    • 开发一种使用修改后的UNet++网络进行子语言图像分割的改进方法.
    • 通过三重网络实现语言下静脉的强有力的色彩分类.
    • 为客观,一维的结果引入基于线性差异分析的颜色定量化.

    主要方法:

    • 修改了UNet++以增强语言下图像和舌头背部细分.
    • 三重网络用于语言下静脉颜色分类.
    • 线性差异分析 (LDA) 用于语言下静脉颜色定量化.

    主要成果:

    • 实现了88.2%的mIoU和94.1%的像素精度,用于舌头背部细分.
    • 实现了69.8%的mIoU和82.7%的像素精度用于舌下静脉细分,分别超过了最先进的5.8%和5.3%.
    • 语言下静脉颜色分类达到了81.2%的整体准确度 (77.5%的少数类回忆);颜色定量化达到了90.5%的准确度.

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    结论:

    • 提出的方法显著提高了语言下图像细分和静脉颜色分析的准确性.
    • 这些进展提供了客观的量化数据,以帮助TCM从业者诊断疾病.
    • 自动化方法为临床决策提供了一个可靠的,非侵入性的工具.