医学图像中的缺血性中风病变细分的深度学习模型:一项调查
Jialin Luo1, Peishan Dai1, Zhuang He1
1School of Computer Science and Engineering, Central South University, Changsha, Hunan, China.
Computers in biology and medicine
|April 27, 2024
概括
深度学习模型在医学图像中显著提升缺血性中风病变细分. 本综述涵盖了改善诊断和治疗规划的模型,数据集和技术.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 神经学 神经学
背景情况:
- 缺血性中风是全球死亡和残疾的主要原因.
- 在MRI和CT扫描中精确细分中风病变对于患者护理至关重要.
- 目前的细分方法需要全面审查,以指导未来的研究.
研究的目的:
- 为缺血性中风病变细分提供深度学习模型的全面审查.
- 讨论成像模式,基准数据集和深度学习架构.
- 确定自动化中风细分方面的挑战和未来研究方向.
主要方法:
- 对用于中风诊断的常见医学成像方法 (MRI,CT) 的审查.
- 对基准数据集的分析:ATLAS,ISLES和AISD.
- 关于深度学习架构 (CNN,变压器) 的概述以及它们对冲击细分的适应.
主要成果:
- 在深度学习中最近的创新概述,用于跨主要数据集的中风细分.
- 对损失函数和数据增强技术的调查.
- 讨论诸如小病变和多式融合等挑战.
结论:
- 深度学习显示了自动化缺血性中风病变细分的重大前景.
- 在模型架构,数据增强和多式联络融合方面的持续研究至关重要.
- 本综述为推进中风细分技术和改善患者治疗结果提供了基础.
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