在CT扫描中使用3D多对象定位和细分CNN的腰椎和胸椎细分
Xiaofan Xiong1, Stephen A Graves2, Brandie A Gross3
1Department of Biomedical Engineering, The University of Iowa, Iowa City, IA 52242, USA.
概括
一种新的自动化方法在CT扫描中使用简单的卷积神经网络 (CNN) 准确地细分单个脊椎. 这项技术通过精确识别骨结构,提高了对癌症的辐射治疗计划.
科学领域:
- 医疗成像医学成像
- 辐射疗法 辐射疗法
- 人工智能的人工智能
背景情况:
- 在计算机断层扫描 (CT) 中精确的脊椎细分对于放射治疗计划至关重要,特别是在影响附近骨结构的癌症中.
- 现有的方法可能需要大量的培训数据,或缺乏临床应用所需的精度.
研究的目的:
- 开发和验证CT扫描中单个腰椎和胸椎的新型自动化3D细分方法.
- 为了实现高细分精度,适合在放射治疗中临床使用.
主要方法:
- 利用单一的,低复杂度的卷积神经网络 (CNN) 架构进行自动化的3D细分.
- 采用基于体积补丁的处理来处理任意的扫描大小和分段脊椎,并在一个步骤中估计中心位置.
- 实施了先进的后处理方案,以提高细分精度.
主要成果:
- 在放射治疗规划的CT扫描中,获得了0.921 ± 0.047的Dice系数和0.271 ± 0.748 mm的签名距离误差.
- 在VerSe2020数据集 (129个CT扫描) 上,获得了0.940 ± 0.065的整体Dice系数和0.109 ± 0.301mm的签名距离误差.
- 与VerSe数据集上验证的其他方法相比,证明了优越的整体细分性能.
结论:
- 拟议的自动化方法在CT扫描中提供了准确和高效的脊椎3D细分.
- 这种方法即使具有有限的特定应用培训数据,也有效,这使得它在临床放射治疗中具有价值.
- 该方法的高精度和稳定性为改善辐射治疗计划提供了显著的潜力.
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