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    科学领域:

    • 神经外科和医学成像学
    • 人工智能在医学中的应用
    • 神经系统疾病 神经系统疾病

    背景情况:

    • 深度大脑刺激 (DBS) 是基本震的关键治疗方法,其疗效取决于精确的电极放置.
    • 针对基本震的最佳 DBS 向与牙 - 脊髓 - 胸膜通道 (DRTT) 有关,使得 DRTT 向成为研究重点.
    • 目前基于通道图的准准确,但对于常规临床使用而言是复杂的,它依赖于结构性MRI (sMRI).

    研究的目的:

    • 开发一种高效且临床上适用的监督机器学习方法,用于DBS中精确的目标定位.
    • 从sMRI数据直接推断出基于DRTT的个性化最佳目标,克服了通道图的局限性.
    • 在神经外科应用中建立一个可泛化目标定位的新框架.

    主要方法:

    • 开发了一个使用卷积神经网络 (CNN) 的两步监督机器学习框架.
    • 该方法将目标本地化视为在减少参考学习框架内的非线性回归问题.
    • 采用了两个基于图像的网络,一个用于分类,一个用于本地化,使用DRTT作为伪地面真相.

    主要成果:

    • 提出的基于CNN的方法在从sMRI中定位最佳DBS目标方面取得了高准确性.
    • 两个测试数据集的平均后部定位误差为2.3毫米和1.2毫米.
    • 平均定位误差为1.7毫米和1.02毫米,超过现有的3D-CNN,解剖学和DRTT图谱方法.

    结论:

    • 开发的框架提供了一种高效和准确的方法,可以从sMRI推断出基于轨道图的DBS目标.
    • 这代表了减少参考学习的新应用,也是第一次尝试从sMRI中定位DRTT.
    • 该方法显示了作为在DBS和其他神经外科手术中目标定位的新基线的潜力.