在急性缺血性中风患者的非对比CT上同时对缺血半阴影和心脏病核心进行细分,使用新型卷积神经网络
Hulin Kuang1, Xianzhen Tan1, Jie Wang1
1Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China.
Biomedicines
|March 28, 2024
概括
一个新的AI模型,I2PC-Net,仅使用非对比计算断层扫描 (NCCT) 来区分可挽救的缺血半阴影 (IP) 和急性缺血中风 (AIS) 患者的心脏核心 (IC),可能取代CT输液.
科学领域:
- 医疗成像医学成像
- 人工智能在医学中的应用
- 神经学 神经学
背景情况:
- 在急性缺血性中风 (AIS) 治疗决策中,精确区分缺血半阴影 (IP) 和心脏病核心 (IC) 是至关重要的.
- 目前的方法,如CT输液 (CTP) 和MRI是资源密集的.
研究的目的:
- 开发和评估一个新的卷积神经网络 (CNN),I2PC-Net,用于同时IP和IC细分,仅使用非对比计算断层扫描 (NCCT).
- 评估NCCT在定量IP和IC评估中作为CTP的替代品的潜力.
主要方法:
- 在编码器中设计的I2PC-Net具有多尺度卷积 (MSC) 和对称增强 (SE) 块.
- 采用基于注意力的解码器,具有层次的深度监督,以改善IP/IC差异化.
- 在AIS患者的197次NCCT扫描中训练并验证了模型.
主要成果:
- I2PC-Net获得了IP的42.76%,IC的33.54%,IP+IC组合的65.67%的点数.
- 损伤体积相关系数为IP的0.95,IC的0.61,IP+IC的0.93 (所有p<0.001).
- 对整体病变体积和IP/IC评估潜力的高度相关性.
结论:
- 基于NCCT的I2PC-Net可以自动对AIS患者的IP和IC进行细分.
- 拟议的方法显示,作为CTP用于定量性中风评估的具有成本效益和可访问性的替代方案,具有前景.
- 在评估IP和IC时,NCCT可以作为CTP的可行的替代品.
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