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用nnUNet放射学在MRI中改善脊髓骨转移的局部化和细分

Yong Xu¹, Chengjie Meng², Dan Chen¹

¹Department of Neurosurgery, Hefei Third Clinical College of Anhui Medical University, The Third People's Hospital of Hefei 230000, China.

Journal of bone oncology

|September 16, 2024

概括

在MRI扫描中,nnUNet放射学模型准确地对脊柱骨转移进行细分,改善了主观医生的划分. 这种自动化方法显示了与手工测量的高度一致性和相关性.

科学领域:

放射学放射学是一门学科.
医疗成像医学成像
人工智能在医学中的应用

背景情况:

在脊髓骨转移的MRI扫描上手动瘤划分的主观变异性影响治疗计划.
准确的细分对于评估脊髓转移的疾病负担和治疗反应至关重要.

研究的目的:

评估nnUNet放射学模型在MRI中自动细分和识别脊髓骨转移的有效性.
为了比较自动细分与手动医生划分的性能.

主要方法:

一组118名脊髓骨转移的患者被追溯分析.
nnUNet的放射学细分模型经过训练,并与医生绘制的瘤边界进行了验证.
用Dice系数和相关性分析来评估细分业绩.

主要成果:

nnUNet模型实现了高的子系数 (0.926训练,0.824测试),证明了有效的本地化和细分,包括小病变.
在不同脊柱区域 (腰部:0.838,胸部:0.785) 中,细分性能强.
在nnUNet和医生细分之间发现了强烈的线性相关性 (R2=0.998,P<0.001).

结论:

nnUNet放射学模型在MRI上对脊髓骨转移的自动定位和细分有效.
自动化细分为手工划分提供了一种一致而准确的替代方案,有可能改善临床工作流程.

关键词:

图像细分图像细分图像细分这就是为什么MRI是MRI.无线电学 (Radiomics) 是一种无线电学.脊椎转移的发生.在联合国网络上.

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Magnetic Resonance Imaging

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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...