用变压器进行心声扫描的左心室细分
Minqi Liao1, Yifan Lian2, Yongzhao Yao1
1Department of Cardiology, Dongguan People's Hospital (The Tenth Affiliated Hospital of Southern Medical Univerity), No 78, Wandao Road, Wanjiang District, Dongguan 523059, China.
Diagnostics (Basel, Switzerland)
|July 29, 2023
概括
使用变压器架构的深度学习模型在回声心电图中显著改善左心室细分,在准确的心功能评估中优于传统的CNN.
科学领域:
- 医学成像分析分析 医学成像分析
- 心脏病学中的人工智能
- 深度学习用于医学诊断.
背景情况:
- 准确的左心室喷射率 (LVEF) 评估对于诊断心脏病至关重要.
- 手动回声心电图分析容易产生人为偏见和高劳动力成本.
- 深度学习,特别是卷积神经网络 (CNN),已经推进了回声心电图细分,但由于CNN的受体场存在局限性.
研究的目的:
- 开发和评估新的深度学习模型,以增强心声图中的左心室 (LV) 分段.
- 为了解决CNN在心声回声学中捕捉大LV区域的局限性.
- 探索基于变压器的架构对于强大的LV细分的有效性.
主要方法:
- 提出了两个纯粹的基于变压器的模型:一个是Swin变压器和K-Net的组合,另一个是使用Segformer.
- 这些模型在EchoNet-Dynamic数据集上进行了训练和评估,用于LV细分.
- 量化指标与现有的基于CNN的模型进行了比较.
主要成果:
- 拟议的变压器模型实现了高平均子相似性得分,为92.92% (Swin变压器-K-Net) 和92.79% (Segformer).
- 两种变压器模型在LV细分精度方面都超过了之前大多数主流CNN模型.
- 这些模型表现出在左心室和左心室之间区分的卓越能力,即使在CNN失败的具有挑战性的情况下也是如此.
结论:
- 基于变压器的模型显示出对准确可靠的心声回声学细分的巨大潜力.
- 这些模型为CNN提供了有效的替代方案,特别是用于对左心室等大型心脏结构进行细分.
- 改进的细分精度有助于更精确的心脏功能评估和疾病诊断.
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