基于深度学习的自动化磁共振图像对腰部结构及其相邻结构在L4/5级别的细分
Min Wang1, Zhihai Su1, Zheng Liu1
1Department of Spinal Surgery, Fifth Affiliated Hospital of Sun Yat-Sen University, 52 Meihua Dong Lu, Xiangzhou District, Zhuhai 519000, China.
Bioengineering (Basel, Switzerland)
|August 26, 2023
概括
这项研究开发了一种深度学习模型,用于从MRI扫描中自动细分腰椎结构. 该模型准确地重建3D模型,帮助手术规划.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 放射学 放射学是一门学科.
背景情况:
- 从MRI中精确细分腰椎结构对于手术规划至关重要.
- 手动细分是耗时的,容易引起观察者之间的变化.
研究的目的:
- 开发和评估一个深度学习模型,用于自动对MRI的多个结构在L4/5脊椎水平上进行细分.
- 评估用于3D模型重建的自动细分的准确性和可靠性.
主要方法:
- 修改后的3D Deeplab V3+深度学习网络被用于自动化细分.
- 使用五倍交叉验证来评估模型性能.
- 子相似系数 (DSC),精度,回忆和皮尔森的相关性被用于评估.
主要成果:
- 深度学习模型的平均DSC为0.886,精度为0.899,回忆率为0.881.
- 在手动和自动3D重建之间的形态测量没有发现任何显著差异.
- 在手动和自动细分测量之间观察到强烈的线性相关性.
结论:
- 使用深度学习的MRI从腰椎结构的自动细分是可行的.
- 这种方法可以通过精确的3D模型重建来促进腰部外科评估.
- 开发的模型显示了提高手术前评估效率和一致性的潜力.
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