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相关概念视频

Computed Tomography01:10

Computed Tomography

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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
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相关实验视频

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腹部CT细分用于使用网络一致性学习评估身体组成.

Shahzad Ali, Yu Rim Lee, Soo Young Park

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 12, 2023
    PubMed
    概括

    本研究引入了网络一致性学习 (NCL) 来改善从CT扫描中估计骨肌肉 (SM) 和脂肪组织. NCL利用未标记的图像,增强身体组成分析,以便更好地预测癌症和手术规划.

    科学领域:

    • 放射学和医学成像学 医学成像学
    • 人工智能在医学中的应用
    • 生物医学图像分析

    背景情况:

    • 准确的骨肌肉 (SM) 和脂肪组织估计对于各种临床场景的预后评估至关重要.
    • 目前的身体组成分析通常依赖于计算机断层扫描 (CT) 扫描,需要像素级语义细分来准确估计SM.
    • 估计全身SM体积可以通过分析单个2D脊椎切片来实现,但需要准确的细分.

    研究的目的:

    • 开发一种高效,具有成本效益的方法来评估身体成分,使用有限的标记图像和大量的未标记的CT图像.
    • 为了提高从腹部CT扫描中对骨肌肉和脂肪组织估计的语义细分的准确性.
    • 评估网络一致性学习 (NCL) 在利用未标记数据以提高细分性能方面的有效性.

    主要方法:

    • 使用标记和未标记的腹部CT切片训练了一个语义细分模型.
    • 采用了两个相同的细分网络,具有不同的重量初始化.
    • 实现了网络一致性学习 (NCL),以强制执行两个网络之间的一致预测,从而从未标记的数据中学习.

    主要成果:

    • 与标准监督细分网络相比,拟议的NCL方法实现了10%更高的子相似系数 (DSC).
    • 证明了NCL在利用大量未标记的CT图像以提高细分精度方面的有效性.

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  • 从新创建的内部数据集中成功分割了腹部CT图像.

    • 结论:

    • 网络一致性学习 (NCL) 通过有效利用未标记的数据,在CT扫描中的身体组成分析中提供了显著的改进.
    • 拟议的方法为快速诊断,预后和干预提供了一种高效和具有成本效益的解决方案.
    • 这种方法通过准确的身体成分评估,促进了改善患者管理.