自主监督的单眼深度估计用于高视野结肠镜摄像机
Alwyn Mathew1, Ludovic Magerand2, Emanuele Trucco2
1Division of Imaging Science and Technology, School of Medicine, University of Dundee, Dundee, United Kingdom.
Frontiers in robotics and AI
|August 10, 2023
概括
这项研究引入了一种自我监督的深度学习模型,用于增强结肠镜检查. 自主软体内机器人提高了在结直肠癌查期间的多检测准确度和患者舒适度.
科学领域:
- 医学成像医学成像
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能是人工智能.
背景情况:
- 光学结肠镜是检测结肠直肠癌的标准,但错过了22% - 28%的息肉,导致间隔癌症.
- 提高聚合物检测率和患者舒适度对于有效的结直肠癌查至关重要.
- 准确的3D环境理解对于自主结肠镜导航和腺瘤检测至关重要.
研究的目的:
- 开发一个自我监督的单眼深度估计模型用于结肠镜检查.
- 为了实现准确的3D环境感知,用于自主结肠镜导航.
- 为了提高腺瘤检测率,并减少在结肠镜检查期间错过的息肉.
主要方法:
- 利用自主监督的深度学习方法直接从结肠镜视频序列进行单眼深度估计.
- 采用视图合成技术来训练模型而不需要地面真实深度地图.
- 调整了模型,以应对结肠镜的特定挑战,包括宽视场摄像头,可变形表面,镜面照明,非兰伯特表面和高遮.
主要成果:
- 该模型成功地通过使用自我监督对结肠镜视频进行深度估计.
- 已证明能够适应广视场相机和具有挑战性的视觉条件.
- 在合成数据集,结肠镜训练模型和真实结肠镜视频上实现了近实时性能,用于深度估计.
结论:
- 自主监督的单眼深度估计是结肠镜中3D场景理解的可行解决方案.
- 这项技术可以显著提高自主结肠镜的精度和系统检查能力.
- 开发的模型为减少遗漏的息肉和间隔癌症提供了一条途径,提高了结直肠癌查结果.
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