支气管GAN:用于视频支气管镜检查的解剖学上一致和域异的图像对图像翻译
Ahmad Soliman1, Ron Keuth1, Marian Himstedt2
1Medical Informatics, University of Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Germany.
概括
支气管GAN通过使用解剖学约束和深度表示来翻译各种数据域来合成现实的支气管镜像. 这种方法产生了大量的数据集,弥合了深度学习应用可用的医学成像方面的差距.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 支气管镜镜像的有限可用性阻碍了深度学习模型的训练.
- 在虚拟,幻影,体内和体外领域进行强大的图像翻译对于临床应用至关重要.
研究的目的:
- 开发BronchoGAN,一种用于合成现实的支气管镜像的新方法.
- 为了使各种支气管镜检查数据领域的图像能够进行强大的翻译.
- 为了生成大规模,现实的支气管镜图像数据集用于训练深度学习模型.
主要方法:
- 整合解剖学约束,特别是支气管孔匹配,进入一个有条件的生成对抗网络 (GAN).
- 使用基础模型生成的深度图像作为中介表示,以提高稳定性和对联数据构建.
- 采用图像对图像翻译技术进行域调整.
主要成果:
- 从各种领域 (虚拟支气管镜,幻影) 的输入图像成功转换为现实的人类呼吸道外观.
- 强大的解剖特征的保存,如支气管孔,在质量和数量上都得到了证明.
- 图像质量指标的显著改进,包括FID,SSIM和Dice系数 (合成图像的改进高达0.43).
结论:
- 支气管GAN有效地通过使用中间深度表示和解剖约束来翻译不同支气管镜领域的图像.
- 该方法可以将公共CT扫描数据 (虚拟支气管镜) 结合起来,创建广泛的,现实的数据集.
- 支气管GAN解决了公共支气管镜像的稀缺问题,促进了人工智能驱动的医学诊断的进步.
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