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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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基于纹理的预处理框架与nnU-Net模型用于准确的内动脉细分.

Kyuseok Kim1, Ji-Youn Kim2

  • 1Institute of Human Convergence Health Science, Gachon University, 191, Hambakmoero, Yeonsu-gu, Incheon 21936, Republic of Korea.

Journal of imaging
|December 24, 2025
PubMed
概括

一种基于纹理的新预处理方法显著增强了使用nnU-Net模型从数字减去血管学 (DSA) 进行的内动脉细分. 这种方法可以提高准确性和拓细节,以便更好地进行神经血管诊断.

关键词:
脑血管抽取方式深度学习是一种深度学习.数字减去血管学图.预处理 预处理质地分析,质地分析.时间序列时间序列.

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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
  • 神经科学是一个神经科学.

背景情况:

  • 从DSA精确细分内动脉对于诊断神经血管疾病和计划治疗至关重要.
  • 目前的深度学习模型显示出希望,但受到预处理技术的限制.
  • 提高预处理是提高血管提取精度的关键.

研究的目的:

  • 开发和评估基于纹理的对比增强预处理框架,与nnU-Net集成,以改善内动脉细分.
  • 评估拟议方法对DSA图像中的细分精度和拓表示的影响.

主要方法:

  • 开发了一个基于纹理的对比度增强框架,将局部对比度,和亮度值地图融合到一个组合的特征面具中.
  • 这个特征面具被用作nnU-Net深度学习模型的输入来进行细分.
  • 在DIAS数据集上使用诸如子相似系数 (DICE) 和跨欧盟交叉点 (IoU) 等指标来评估性能.

主要成果:

  • 拟议的方法实现了0.83 ± 0.20的DICE和0.72 ± 0.14的IOU,超过了CLAHE和基线方法.
  • 在容器连接 (VC) 和拓准确性方面观察到显著的改进,VC相对于未经处理的图像下降了65%以上.
  • 与现有方法相比,基于纹理的预处理显示出强度和耐噪能力.

结论:

  • 将基于纹理的预处理与nnU-Net集成,大大提高了DSA的内动脉细分.
  • 该方法提供了强大,耐噪声和临床可解释的结果,推进了神经血管诊断能力.
  • 这种方法比医学成像中血管细分的传统方法有显著的改进.