伴心膜细分和跟踪从使用深度学习的胸前回声心脏成像
Sigurd Vangen Wifstad1, Henrik Agerup Kildahl2, Bjørnar Grenne2
1Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway.
Ultrasound in medicine & biology
|February 10, 2024
概括
深度学习自动化了从心声图 (echocardiograms) 中对心肌 (mitral valve,MV) 的分析,提高了心脏膜疾病 (VHD) 诊断的准确性和效率. 这种方法增强了定量评估,帮助治疗决策和监测.
科学领域:
- 心脏病学 心脏病学
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 膜心脏病 (VHD) 是一个重大的健康挑战.
- 准确评估心脏门功能对于治疗规划至关重要.
- 胸外心声学 (TTE) 对于VHD评估至关重要,但往往是主观的和耗时的.
研究的目的:
- 通过使用深度学习,自动从回声心脏图中提取 mitra (MV) 传单和环形点.
- 为了提高标准化和减少MV疾病的定量评估工作量.
主要方法:
- 来自127名患者的2931张图像中的MV传单和annulus点的注释.
- 开发一个注意力UNet模型,具有深度监督和注意力系数调度,用于特征细分.
- 在整个心脏周期中从MV传单贝的细分口罩中提取定量生物标志物.
主要成果:
- 获得了0.63 ± 0.14的子得分,3.64 ± 2.53毫米的圆环误差和8.7 ± 8.3°的传单角度误差.
- 注意UNet提高了临床相关指标的稳定性,减少了与标准UNet相比的标准偏差.
- 通过使用衍生生物标志物成功识别了MV脱落,狭窄和健康病例.
结论:
- 深度学习细分和MV形态和运动的跟踪是可行的,使用注意力门和深度监督.
- 这种方法显示了增强VHD诊断和治疗监测的希望.
- 自动化定量评估可以带来更标准化和更有效的VHD评估.
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