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基于深度学习的结构细分和肌内脂肪注释在腰部磁共振成像上

Yefu Xu¹, Shijie Zheng¹, Qingyi Tian¹

¹Department of Spine Surgery, ZhongDa Hospital, School of Medicine Southeast University Nanjing China.

|September 18, 2024

概括

这项研究开发了一种自动化的双模型,用于在MRI扫描上分析腰椎间盘 (LDH). 该模型准确地细分副脊髓肌肉并识别脂肪透 (FI),优于传统的值方法.

科学领域:

医疗成像医学成像
人工智能在医学中的应用
脊柱诊断脊柱诊断脊柱诊断脊柱诊断

背景情况:

腰椎间盘 (LDH) 是腰部疼痛的一个常见原因.
在LDH患者中,通常会出现副脊髓肌肉缩和脂肪透 (FI),症状恶化.
磁共振成像 (MRI) 对于评估副脊柱肌肉状况至关重要.

研究的目的:

开发一种用于自动化肌肉细分和FI注释在腰椎MRI上的双模型.
帮助临床医生全面评估LDH状况和相关的肌肉变化.

主要方法:

追溯收集LDH患者数据 (2020年12月 - 2022年5月).
开发和验证肌肉细分和FI注释的双模型.
使用诸如子相似系数 (DSC),平均精度 (AP),回忆,F1得分,科恩卡帕和平均绝对百分比误差 (MAPE) 等指标进行性能评估.
将模型衍生FI测量与使用MAPE的值算法进行比较.

主要成果:

肌肉细分模型在内部测试组中实现了0.92的DSC和0.98的AP.
脂肪注释模型的回忆率达到91.30%和F1评分为0.82.
开发的模型在FI测量中显示了较低的MAPE,与跨脊肌的值算法相比.

结论:

开发的双模型在自动化肌肉细分和腰椎MRI上的FI注释方面表现出色.
与现有的值技术相比,该模型提供了一种更准确的方法来量化在副脊髓肌肉中的脂肪透.
这种工具可以帮助临床医生更好地评估与LDH相关的肌肉变化,并改善患者管理.

关键词:

深度学习是一种深度学习.脂肪的透脂肪的透腰椎椎间盘的发生副脊柱肌肉副脊柱肌肉副脊柱肌肉细分化细分化的细分化

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