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通过机器学习分析超声波图像的组织个别签名

Katarzyna Heryan, Joanna Sorysz, Michael Friebe

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|December 3, 2025

概括

这项研究表明,机器学习可以使用超声波成像来分类具有组织状性质的材料. 这项技术可以改善手术工具导航和手术过程中的实时组织识别.

科学领域:

医疗成像医学成像
机器学习机器学习
手术技术手术技术

背景情况:

最少侵入性手术受益于先进的传感器和数据科学,但手术内精度和工具定位仍然具有挑战性.
超声波成像可以提供实时可视化,但由于文物和仪器对齐不佳而受到影响.
目前的方法在手术期间缺乏强大的实时组织特征.

研究的目的:

利用超声波和机器学习开发一种初步框架来对具有组织状性质的材料进行分类.
评估基于超声波的材料分类对改善手术内导航的潜力.
通过对超声数据的统计学习来探索实时组织特征.

主要方法:

人工幽灵和生物组织用超声波成像.
图像特征被提取并使用Gabor,Tamura和Hessian过器进行预处理.
机器学习模型 (随机森林,SVM,差异分析) 被训练并使用F1分数进行评估.

主要成果:

开发的框架在对具有组织状性质的材料进行分类时获得了高达0.8的F1评分.
统计学学习与超声波成像相结合,证明了对材料进行区分的潜力.
初步结果显示,增强手术内导航和组织表征有望得到改善.

结论:

超声波成像与统计学学习相结合,可以区分具有组织相关性质的材料.
这种方法有可能改善手术内导航和实时组织识别.
未来的研究将专注于扩大数据集和临床整合.

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