结合神经网络和图像合成,使得高极化XeMRI的胸腔自动细分能够实现,而无需进行质子扫描
Suphachart Leewiwatwong1, Junlan Lu2, Isabelle Dummer3
1Center for In Vivo Microscopy, Duke University Medical Center, Durham, NC, USA; Department of Biomedical Engineering, Duke University, Durham, NC, USA.
Magnetic resonance imaging
|July 5, 2023
概括
合成有缺陷的129 (Xe) MRI图像加速了用于胸腔细分的训练数据的创建. 用合成数据训练的3D模型实现了高细分精度,即使在缺陷区域.
科学领域:
- 医疗成像医学成像
- 人工智能在医学中的应用
- 肺部医学 肺部医学
背景情况:
- 准确的胸腔细分对于量化129- (Xe) MRI至关重要.
- 使用1H和129-Xe扫描进行手动细分是耗时的.
- 外围通风缺陷使自动细分复杂化.
研究的目的:
- 开发一种用于胸腔细分的自动化方法,仅使用129-XeMRI.
- 通过合成具有缺陷的129-Xe图像来加速对细分模型的训练数据的创建.
- 训练和评估用于细分的3D卷积神经网络 (CNN).
主要方法:
- 合成了各种缺陷的129-Xe通风图像,以增强训练数据集.
- 训练有素的基于2D U-net和3D V-net的细分模型.
- 使用了子焦点和解剖约束损失函数.
- 使用Dice系数比较细分性能.
主要成果:
- 包括合成数据改善了U-net和3D模型的细分性能.
- 3D模型获得了0.929.9的卓越的整体子得分.
- 综合数据显著提高了通风缺陷区域的细分精度.
结论:
- 仅使用129-XeMRI进行高质量的胸腔细分是可行的.
- 用合成数据训练的3D模型为自动细分提供了一个有希望的方法.
- 这种方法加快了细分过程并提高了准确性,特别是在具有通风缺陷的具有挑战性的情况下.
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