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基于深度学习的可扩展光纤探针的图像超分辨率,用于食道癌症诊断

Xiaohui Zhang¹, Mimi Tan², Mansour Nabil²

¹University of Illinois Urbana-Champaign, Department of Bioengineering, Urbana, Illinois, United States.

Journal of biomedical optics

|April 8, 2024

概括

一种新的可扩展终端光纤探针与基于深度学习的超高分辨率成像相结合,提高了食道癌查效率. 这种方法提高了可视化和诊断准确度,为早期癌症检测提供了有前途的工具.

科学领域:

医学成像医学成像
光学工程的光学工程.
人工智能的人工智能是人工智能.

背景情况:

对食道癌 (EC) 的内镜查对于早期诊断和治疗至关重要.
目前的光学微内镜视野有限,阻碍了高效的大面积查.
提高内镜查的效率是一个重大的临床挑战.

研究的目的:

引入一种新的可扩展终端光纤探头,用于在EC查中增强可视化.
评估基于深度学习的超分辨率 (DL-SR) 方法,以克服微内镜中的有限采样.
提高内镜食道癌查的效率和诊断能力.

主要方法:

开发了一个终端可扩展光纤探头概念,用于更大的视野.
将DL-SR应用于模拟的低分辨率微内镜图像,以生成超分辨率 (SR) 图像.
使用不同的图像退化模型优化探测器参数,并通过人类病理学读取研究进行验证.

主要成果:

在不同的退化参数中,DL-SR表现出不同程度的图像质量改善.
超高分辨率图像被内镜学家解释,其准确度与高分辨率图像相美.
末端可扩展探头概念显示了改进EC选的可行性.

结论:

DL-SR和末端可扩展探头的组合为高产量EC查提供了一个有希望的方法.
这项技术有可能改善用于临床应用的稀疏图像重建.
进一步开发可能会导致更有效,更准确的内镜诊断工具.

关键词:

巴雷特的食道.基于深度学习的超级分辨率.降解模型的降解模型.终端可扩展的光纤探测器末端显微镜的使用方法这是食道癌的癌症.微内镜检查是微内镜检查的一种.

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