在延迟增强MRI中对心脏病发作进行细分,使用概率图和基于变压器的神经网络
Erwan Lecesne1, Antoine Simon1, Mireille Garreau1
1Univ Rennes, Inserm, LTSI - UMR 1099, Rennes, 35000, France.
Computer methods and programs in biomedicine
|October 21, 2023
概括
这项研究引入了一种新的方法,用于使用概率图和U-Net变压器对心肌梗塞 (MI) 进行细分. 该方法实现了高精度,超过了心脏MRI中精确MI量化的现有技术.
科学领域:
- 医疗成像医学成像
- 人工智能在医学中的应用
- 心血管研究研究心血管研究
背景情况:
- 准确量化心肌梗塞 (MI) 对患者管理至关重要.
- 晚期加多增强心脏MRI (LGE-MRI) 是MI评估的标准.
- 在LGE-MRI中对心脏病发作的组织进行细分是具有挑战性的,原因是图像工件.
研究的目的:
- 开发一种用于精确分段心肌梗塞的自动化方法.
- 改进使用LGE-MRI进行MI的定量评估.
- 为了克服当前MI细分技术的局限性.
主要方法:
- 利用健康心肌的概率图来指导心脏梗塞的局部化.
- 组合的2D U-Net和U-Net变压器用于细分.
- 在U-Net变压器网络中将概率图作为额外输入.
- 开发了一种适应的损失函数,以提高轮分割的准确性.
主要成果:
- 在心肌细分方面获得了92.94%的子得分.
- 在心脏病发作细分方面获得了92.36%的子得分.
- 在2020年MICCAI EMIDEC挑战数据集中证明了竞争性表现.
结论:
- 拟议的方法超过了对心脏病发作细分的最新技术.
- 整合一个轮损失函数对于改进细分至关重要.
- 这种方法在LGE-MRI中为MI量化提供了卓越的性能.
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