胎盘的形状感知细分在BOLD胎儿MRI时间序列中的胎盘细分
S Mazdak Abulnaga1, Neel Dey2, Sean I Young3
1CSAIL/EECS, Massachusetts Institute of Technology, Cambridge, MA, USA; MGH/HST Martinos Center for Biomedical Imaging, Harvard Medical School, Boston, MA, USA.
The journal of machine learning for biomedical imaging
|October 29, 2024
概括
在BOLDMRI中精确的胎盘细分对于评估胎儿健康至关重要. 本研究介绍了一种机器学习框架,可以提高细分精度,特别是在胎盘边界附近,有助于更好的功能评估.
科学领域:
- 生物医学工程 生物医学工程
- 医疗成像医学成像
- 胎儿医学 胎儿医学
背景情况:
- 血液中氧气水平依赖 (BOLD) 的MRI与母体高氧症评估胎盘功能.
- 精确的胎盘细分对于BOLDMRI分析至关重要,但由于器官不刚性和运动工件而具有挑战性.
- 目前的细分方法与胎盘复杂的形状和边界作斗争.
研究的目的:
- 开发和评估一个机器学习细分框架,用于胎盘BOLDMRI.
- 为了提高在BOLDMRI时间序列中的时胎盘细分的准确性.
- 为了应对胎盘形状,变形和边界定义所带来的挑战.
主要方法:
- 提出了一种机器学习分段框架用于胎盘BOLDMRI.
- 实施了胎盘边界加权损失配方.
- 在91名患有各种胎儿和母亲疾病的受试者身上训练并测试了该模型.
主要成果:
- 拟议的模型可靠地分隔了胎盘体积在正常和高氧BOLDMRI时间序列中.
- 边界加权显著改善了细分性能,使子系数增加了8.3% (交叉) 和6.0% (符号距离转换).
- 该框架在健康胎儿,胎儿生长限制和高孕产妇BMI队列中表现出强度.
结论:
- 开发的机器学习框架提高了 BOLD MRI 中的胎盘细分精度.
- 边界加权损失有效地改善了像胎盘这样的非刚性器官的细分.
- 这种改进的细分方便更可靠地评估胎盘氧化和功能.
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