一个系统的文献综述:用于合成医学图像生成的深度学习技术及其在放射治疗中的应用
Moiz Khan Sherwani1, Shyam Gopalakrishnan1
1Section for Evolutionary Hologenomics, Globe Institute, University of Copenhagen, Copenhagen, Denmark.
Frontiers in radiology
|April 11, 2024
概括
深度学习 (DL) 算法显示为合成计算机断层扫描 (sCT) 生产的临床替代方案. 本综述分析了基于DL的sCT方法,强调了它们日益普及和临床应用的潜力.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 放射学 放射学是一门学科.
背景情况:
- 合成计算机断层扫描 (sCT) 对于放射治疗规划至关重要.
- 传统的sCT生成方法具有局限性.
- 深度学习 (DL) 提供了改善sCT生成的潜力.
结论:
- 基于DL的sCT生成正在获得研究中的引力.
- 需要进一步评估,以确定DL方法的临床准备.
- DL对医疗成像技术的发展具有前途.
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