结肠镜3D视频数据集与2D-3D注册的配对深度
Taylor L Bobrow1, Mayank Golhar1, Rohan Vijayan1
1Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.
Medical image analysis
|September 15, 2023
概括
一个新的数据集和注册方法使得3D计算机视觉用于结肠镜的定量评估成为可能. 这推动了人工智能工具的开发,以使用现实世界的视频数据改进结肠癌查.
科学领域:
- 医疗成像医学成像
- 计算机视觉 计算机视觉
- 胃肠病学 胃肠病学
背景情况:
- 3D计算机视觉技术对于结肠镜检查至关重要,但由于基本真相数据稀缺,缺乏定量评估.
- 目前用于分析结肠镜视频的现有方法主要是定性,阻碍了客观的基准测试.
研究的目的:
- 引入高保真性结肠镜3D视频数据集 (C3VD) 用于对3D计算机视觉方法进行基准测试.
- 介绍一种新型的多式二维三维注册技术,用于准确地将结肠镜视频与地面真实3D模型对齐.
主要方法:
- 开发了一种多式2D-3D注册技术,使用生成对抗网络进行图像到深度的转换和边缘特征对齐的进化优化.
- 注册了22个视频序列,生成了10,015,包含包括深度,表面正常值和6-DoF姿势在内的全面地面真相数据.
- 在模拟中验证了注册准确性,实现了较低的翻译和旋转错误,通过利用视频信息显著改进.
主要成果:
- 多式联网登记方法在模拟中实现了0.321毫米的平均翻译误差和0.159度的旋转误差.
- 使用视频信息提高了注册准确度,在翻译方面提高了55%以上,在旋转方面提高了60%,与单方法相比.
- C3VD数据集包括10,015个注册,包括广泛的地面真相,以及带有姿势和表面模型的临床查视频.
结论:
- C3VD数据集和新型注册技术为定量评估和开发结肠镜中的3D计算机视觉提供了一个强大的平台.
- 该资源促进了人工智能驱动工具的进步,以实现更准确和客观的结肠癌查.
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