在肺部成像中对人工智能的调查
Punam K Saha1, Syed Ahmed Nadeem2, Alejandro P Comellas3
1Departments of Radiology and Electrical and Computer Engineering, University of Iowa, Iowa City, IA, 52242.
概括
深度学习 (DL) 彻底改变了COPD和癌症等肺部疾病的肺部成像分析. 本调查详细介绍了DL在医学成像中的应用,重点是细分肺部解剖学,以便更好地诊断和研究.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 肺部医学 肺部医学
背景情况:
- 深度学习 (DL) 已经改变了计算机视觉和人工智能应用.
- 肺部疾病影响全球人口的很大一部分.
- 医学成像在了解肺部疾病及其进展方面发挥着至关重要的作用.
研究的目的:
- 调查DL在肺部成像中的应用.
- 突出DL在诊断和管理肺部疾病中的作用.
- 专注于基于DL的肺部和胸部解剖学的细分.
主要方法:
- 对DL架构和用于肺部成像的方法的审查.
- 对DL进行图像分类,识别,注册和细分的分析.
- 重点是肺体积,叶片,血管,呼吸道和胸部肌肉骨结构的细分.
主要成果:
- 在肺部疾病的AI驱动医学成像中,DL提供了显著的机会.
- 在各种肺部图像处理任务中,DL技术是有效的.
- 基于DL的解剖结构细分是一个关键的应用领域.
结论:
- DL是推动肺部研究和临床实践的强大工具.
- 使用DL精确细分肺和胸部解剖学有助于疾病的理解.
- 这份调查提供了肺部成像中DL的全面概述.
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