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Updated: Jun 4, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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用于医疗图像分割的动态域泛化.

Zhiming Cheng1, Mingxia Liu2, Chenggang Yan1

  • 1School of Communication Engineering, Hangzhou Dianzi University, Hangzhou, 310018, China.

Neural networks : the official journal of the International Neural Network Society
|December 29, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了用于医疗图像细分的动态域泛化 (DDG),通过动态调整参数和使用全球-本地风格转换来改进新数据的模型性能. 这种方法提高了医疗图像分析的稳定性.

关键词:
数据增强数据增强域名通用化 域名通用化富里叶变换是什么意思 富里叶变换医疗图像细分 医疗图像细分位置编码 位置编码

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科学领域:

  • 医学图像分析 医学图像分析
  • 计算机视觉 计算机视觉
  • 机器学习 机器学习

背景情况:

  • 基于域泛化医疗图像细分 (DGMIS) 旨在提高对未见数据的模型稳定性.
  • 现有的DGMIS方法经常使用静态模型和全局风格转换,限制适应目标域变异.
  • 挑战包括缺乏动态适应和不充分捕捉局部图像细节.

研究的目的:

  • 为医疗图像细分提出一个动态域泛化 (DDG) 方法.
  • 通过动态参数调整和风格模拟,增强在未见的目标域上的模型概括能力.
  • 解决目前DGMIS中静态模型和全局仅样式增强的局限性.

主要方法:

  • 开发了一个动态位置转移 (DPT) 模块,以解静态和动态模型参数,并结合定位编码以适应.
  • 引入了一个全球-本地福里埃随机转换 (GLFRT) 模块,以捕获全球和本地风格信息,增强样本多样性.
  • 在GLFRT中使用随机风格选择策略来平衡多样性和计算成本.

主要成果:

  • 拟议的DDG方法在多个公共医学图像数据集上表现出比最先进的方法更高的性能.
  • 在Fundus上获得了0.58%的Dice平均得分改善,在前列腺上达到0.76%,在SCGM数据集上达到0.76%.
  • 实验验证证了动态适应和全球-本地风格模拟的有效性.

结论:

  • DDG方法显著提高了医疗图像细分模型的概括能力.
  • 动态参数调整和集成的全球-本地风格转换是跨域 robust 细分的关键.
  • 该方法为在各种临床环境中部署医疗图像细分模型提供了一个有希望的解决方案.