评估注释专业知识对基于人工智能的超声波细分的影响:对左前庭附属体的案例研究
Rafael Fernandes1, João L Vilaça1,2, Helena R Torres1,2
12Ai-School of Technology, IPCA, Campus do IPCA, Vila Frescaínha S. Martinho, 4750-810, Barcelos, Portugal.
Cardiovascular engineering and technology
|January 6, 2026
概括
人工智能细分模型在医学成像中的非系统性错误方面表现良好,模仿观察者变化. 然而,系统性错误会对准确性产生重大影响,这凸显了人工智能开发中需要精确的基准真实数据的需求.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 生物医学工程 生物医学工程
背景情况:
- 人工智能驱动的医疗图像细分至关重要,但受到稀缺,手动注释的地面真相数据的限制.
- 目前最先进的监督学习,严重依赖于专家生成的注释,这可能是耗时和主观的.
- 像超声波这样具有挑战性的模式,图像质量和文物低,加剧了注释困难.
研究的目的:
- 评估用户专业知识对基准事实注释准确性的影响.
- 评估注释质量如何影响AI细分模型的性能.
- 调查AI细分对系统和非系统注释错误的稳定性.
主要方法:
- 在超声波图像中创建了两组用于左心房附属体 (LAA) 分段的数据集:一组由专家创建,一组由初学者创建.
- 通过在手册注释中引入系统和非系统错误来生成合成数据集.
- 使用nnU-Net框架在每个数据集上训练细分模型,对专家测试标签进行性能评估.
主要成果:
- 在专家和新手注释上训练的AI模型获得了可比的Dice分数 (0.81 ± 0.09对比0.77 ± 0.12).
- 在与非系统性错误进行训练时,没有观察到显著的绩效差异.
- 在注释中引入的系统错误导致了与手动轮相比,在统计学上显著的性能下降.
结论:
- 人工智能细分模型显示出高效率,特别是在处理模仿观察者间变化的非系统注释变化方面.
- 模型面临系统性错误,这表明需要高质量,准确的基准真相数据.
- 这些发现强调了AI的稳定性,但也突出了当面对训练数据中一致的,结构化的不准确时的局限性.
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