基于深度学习的MRI,CT和PET的合成:审查和分析
Sanuwani Dayarathna1, Kh Tohidul Islam2, Sergio Uribe3
1Department of Data Science and AI, Faculty of Information Technology, Monash University, Clayton VIC 3800, Australia.
Medical image analysis
|December 5, 2023
概括
深度学习模型可以从其他模式生成合成医学图像 (CT,MRI,PET),改善临床工作流程. 本调查回顾了2018-2023年医疗图像合成的进展,强调了深度学习技术.
科学领域:
- 医学成像和人工智能.
- 计算机视觉和机器学习在医疗保健中的应用.
背景情况:
- 医学图像合成旨在从另一个图像模式 (例如CT,MRI,PET) 中生成一种图像模式,以增强临床决策和克服获取挑战.
- 由于复杂的非线性域映射,深度学习生成模型在医学图像翻译方面表现优越,与传统方法相比.
研究的目的:
- 从2018年到2023年全面审查基于深度学习的医学成像翻译技术.
- 分析各种合成方法,网络架构 (CNN,变压器,扩散模型),损失函数,数据集和性能指标.
- 确定当前的挑战,并建议在合成医学图像生成领域的未来研究方向.
主要方法:
- 基于深度学习的医学图像合成方法的系统文献综述.
- 分析网络架构,包括卷积神经网络 (CNN),变压器和扩散模型.
- 对损失函数,数据集,解剖区域,图像质量和下游任务性能进行比较评估.
主要成果:
- 深度学习模型,特别是生成模型,已经显著提升了伪CT,合成MR和合成PET的医疗图像合成.
- 像变压器和扩散模型这样的新型架构显示出超越传统CNN的前景.
- 性能因模型设计,损失函数,数据集和解剖学焦点而异,图像质量和下游任务实用性是关键评估标准.
结论:
- 深度学习为医学图像合成提供了强大的工具,解决了模式获取的局限性.
- 对新型架构的持续研究和标准化的评估对于临床翻译至关重要.
- 这项调查为研究人员导航医疗图像合成不断变化的景观提供了路线图.
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