在心脏超声波中进行无标签细分的自我监督学习
Danielle L Ferreira1,2, Connor Lau1,2, Zaynaf Salaymang1
1Department of Medicine, Division of Cardiology, University of California, San Francisco, 521 Parnassus Avenue, San Francisco, CA, USA.
Nature communications
|April 30, 2025
概括
这项研究引入了用于心脏超声波分析的自我监督深度学习管道,消除了手动注释的需要. 该方法准确地划分心室,并计算测量,证明临床有效和可扩展.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 心脏病学 心脏病学
背景情况:
- 超声波对心室细分至关重要,但由于手工劳动和可重现性问题而具有挑战性.
- 监督深度学习方法需要手动注释,这耗时且容易出现错误.
研究的目的:
- 为心脏超声波分析开发一个自我监督的细分管道.
- 为了使心室的准确和可重复测量,无需手动标签.
- 验证开发方法的临床实用性和可扩展性.
主要方法:
- 开发了一个自我监督的细分管道,结合了计算机视觉,临床知识和深度学习.
- 该管道在450个心声回声图上进行了训练,并在18,423个心声回声图上进行了验证,包括外部数据集.
- 通过将管道衍生测量与临床测量和心脏MRI (黄金标准) 进行比较来评估性能.
主要成果:
- 该管道在细分心室方面取得了很高的准确性,左心室的平均Dice分数为0.89.
- 测量预测准确性 (r2=0.55-0.84) 与临床间变异性和监督学习可比.
- 管道和MRI测量之间的相关性与临床心声谱的相关性相似.
结论:
- 开发的自主监督管道为心脏超声波细分和测量提供了临床有效,无手动标签和可扩展的解决方案.
- 这种方法显著减少了劳动力,并提高了心室分析的可重复性.
- 该方法对广泛的临床采用和心声回声学方面的进步充满希望.
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