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在超声波成像中自动测量隔膜厚度的边缘感应扩散细分模型
IEEE journal of biomedical and health informatics
|August 22, 2025
概括
本研究提出了一个边缘感应扩散细分模型 (ESADiff) 来精确测量隔膜厚度. 这种新方法增强了带细分,提高了呼吸系统疾病的诊断可靠性.
科学领域:
- 医学成像分析
- 生物医学工程
- 计算解剖学
背景情况:
- 隔膜厚度是评估呼吸功能的关键指标.
- 手动超声波测量是主观的,缓慢的,容易产生噪音.
- 临床上需要准确可靠的隔膜厚度量化.
研究的目的:
- 开发一种使用超声波测量隔膜厚度的自动,准确和可靠方法.
- 为了更好的生物识别分析,在超声波图像中改善带细分.
- 解决手动测量的局限性,包括主观性和噪声敏感性.
主要方法:
- 引入了具有边缘意识的扩散细分模型 (ESADiff),结合了结构面膜知识.
- 采用了一种具有代无声化的扩散模型,用于边缘保护特征学习.
- 开发了一个异型边缘敏感注释精细化模块和具有边缘前排列损失函数的曲率感知可变形卷曲.
主要成果:
- 与最先进的技术相比,ESADiff实现了更精细的护膜细分.
- 显著提高了隔膜厚度测量的准确性和可靠性.
- 在隔膜,小腿和舌头肌肉超声波数据集中表现出强大的概括性.
结论:
- 提出的ESADiff模型提供了超声波对隔膜厚度测量的优越方法.
- 该方法有效地利用先前的结构和形状知识进行增强的细分.
- ESADiff显示了改善呼吸功能临床评估的显著潜力.
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