基于深度学习的医学超声波图像和视频细分方法:概述,边界和挑战
Xiaolong Xiao1,2, Jianfeng Zhang1,3, Yuan Shao1,2
1College of Mathematical Medicine, Zhejiang Normal University, Jinhua 321004, China.
Sensors (Basel, Switzerland)
|April 26, 2025
概括
本综述探讨了超声波图像细分的深度学习,涵盖了经典和先进的方法,如扩散模型. 它旨在通过详细介绍医学成像分析的技术,数据集和未来方向来指导研究人员.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 超声波成像由于文物和噪音而存在细分挑战.
- 深度学习正在彻底改变医学图像处理和分析.
研究的目的:
- 审查基于深度学习的超声波图像和视频的细分方法.
- 为了分类和评估最近的进步,包括扩散和细分任何模型.
- 为该领域的研究人员提供全面的概述.
主要方法:
- 将深度学习细分方法分为四类的分类.
- 基于方法的特点对方法的评估.
- 审查常见的超声波数据集和评估指标.
主要成果:
- 总结了各种深度学习细分方法的成就和局限性.
- 突出了新模式的影响,如扩散和细分任何东西.
- 确定了超声波图像细分方面的关键趋势和挑战.
结论:
- 深度学习为准确的超声波细分提供了强大的工具.
- 了解方法的优点和缺点对于进步至关重要.
- 未来的研究应该解决改善临床应用的剩余挑战.
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