使用转移窗口多层感知子混合器网络进行心脏MRI细分
Elham Abouei1, Shaoyan Pan1,2, Mingzhe Hu1,2
1Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, United States of America.
Physics in medicine and biology
|May 14, 2024
概括
一个新的深度学习算法,转移窗口多层感知子 (Swin-MLP) 混合器网络,在MRI扫描中准确地细分心脏结构. 这种先进的方法改进了现有的技术,以简化临床工作流程.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 心脏病学 心脏病学
背景情况:
- 在磁共振成像 (MRI) 中精确细分心脏结构对于诊断和管理心血管疾病至关重要.
- 当前的深度学习模型在实现左心室,右心室和心肌的精确和高效细分方面面临着挑战.
研究的目的:
- 开发和评估一种用于心脏MRI的新型深度学习细分算法.
- 为了提高左心室,右心室和心肌 (Myo) 的细分的准确性和效率.
主要方法:
- 提出了一个转移窗口多层感知子 (Swin-MLP) 混合器网络,使用3D U形编码器-解码器架构.
- 该网络的培训和评估是基于来自100个个人的公共心脏MRI数据.
- 通过使用子得分系数,精度,灵敏度,豪斯多夫距离 (HD),平均表面距离 (MSD) 和剩余平均平方距离 (RMSD) 来定量评估性能.
- 拟议的网络与动态UNet和Swin-UNetr算法进行了基准测试.
主要成果:
- 斯温-MLP网络实现了高体积相似度指标:子 = 0.952 ± 0.017,精度 = 0.948 ± 0.016,灵敏度 = 0.956 ± 0.022.
- 平均表面相似度指标是HD = 1.521 ± 0.121毫米,MSD = 0.266 ± 0.075毫米,以及RMSD = 0.668 ± 0.288毫米.
- 拟议的网络在大多数体积和表面指标上显示出比动态UNet和Swin-UNetr的统计学上显著的改善 (p <0.05).
结论:
- 与当前领先的方法相比,Swin-MLP混合器网络提供了卓越的心脏MRI细分精度.
- 这种强大的算法有可能优化心血管成像中的临床工作流程.
- 这些发现突显了Swin-MLP在精确划定心脏结构方面的有效性.
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