机器学习用于医疗图像翻译:系统性审查
Jake McNaughton1, Justin Fernandez1,2, Samantha Holdsworth3,4,5
1Auckland Bioengineering Institute, University of Auckland, 6/70 Symonds Street, Auckland 1010, New Zealand.
Bioengineering (Basel, Switzerland)
|September 28, 2023
概括
深度学习产生合成医学图像,主要是MRI到CT扫描,用于改进诊断和MRI-only辐射治疗. 需要更多关于CT到MRI合成和数据集可用性的研究.
科学领域:
- 医学成像医学成像
- 人工智能的人工智能是人工智能.
- 放射学 放射学是一门学科.
背景情况:
- 计算机断层扫描 (CT) 扫描对于初步的神经学评估是具有成本效益的.
- 磁共振成像 (MRI) 为检测异常提供了卓越的细节.
- 深度学习 (DL) 越来越多地用于医学图像合成.
研究的目的:
- 审查利用深度学习用于合成医学图像生成的研究.
- 分析MRI到CT和CT到MRI合成中的趋势.
- 识别医学图像合成研究中的动机和局限性.
主要方法:
- 在2023年3月进行了全面的文献搜索.
- 选择和分析了自2017年以来发表的103项相关研究.
- 分析包括出版年份,数据集大小,模式,DL架构,动机和评估方法.
主要成果:
- 74%的研究集中在MRI到CT合成上,通常用于MRI-only放射治疗.
- 其他合成方向包括CT到MRI,交叉MRI,PET到CT和MRI到PET.
- 关键的动机是支持仅用MRI进行放射治疗,帮助诊断和增强数据集.
结论:
- 尽管后者的好处,但MRI到CT合成比CT到MRI更受研究.
- 医疗数据集的有限可用性和规模,特别是配对的数据集,构成了重大挑战.
- 建议包括组建一个全球数据集获取联盟,并建立在临床实践中用于合成图像的标准化评估方法.
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