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Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
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一种基于深度学习的自动细分和3D可视化技术,用于使用计算机断层扫描图像检测内出血.

Muntakim Mahmud Khan1, Muhammad E H Chowdhury2, A S M Shamsul Arefin1

  • 1Department of Biomedical Physics and Technology, University of Dhaka, Dhaka 1000, Bangladesh.

Diagnostics (Basel, Switzerland)
|August 12, 2023
PubMed
概括

这项研究开发了一种机器学习算法,可以在CT扫描上检测内出血 (ICH),从而在识别头骨内出血方面达到高精度,从而改善了患者的诊断和护理.

关键词:
子相似系数 (DSC) 是指子的相似系数.计算机断层扫描 (CT) 是一种计算机断层扫描.卷积神经网络的神经网络.深度学习是一种深度学习.交叉点与联合点 (IoU) 的交叉点内出血 内出血

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科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 神经外科 神经外科

背景情况:

  • 内出血 (ICH) 由于严重程度和形态的不同,存在诊断挑战,有可能错过小出血的诊断.
  • 计算机断层扫描 (CT) 是诊断ICH的标准,可以进行快速,救命的干预.
  • 由于高死亡率和残疾率,准确及时检测ICH至关重要.

研究的目的:

  • 开发和评估用于检测内出血 (ICH) 的机器学习算法,使用普通CT图像.
  • 为了比较不同深度学习模型对出血细分的性能.

主要方法:

  • 75名患者的CT图像使用脑窗,骨剥离和图像反转进行了预处理.
  • 使用U-Net,U-Net++和特征金字塔网络 (FPN) 模型进行了出血细分.
  • 一个带有DenseNet201编码器的U-Net模型表现出卓越的性能.

主要成果:

  • 使用DenseNet201的U-Net模型实现了最高的子相似系数 (DSC) 和交叉点在联盟 (IoU) 上的得分.
  • 创建了一个3D大脑模型,以可视化预测出血与地面真相相比.
  • 进行了对出血大小的体积测量.

结论:

  • 开发的机器学习算法显示了在临床实践中准确检测ICH的希望.
  • 使用DenseNet201编码器的U-Net模型在CT扫描上对出血细分有效.
  • 3D可视化和体积分析有助于评估ICH严重程度.