概括
这项研究引入了一种弱监督的方法,以实现纤维束内镜的3D感知,这对于机器人导航至关重要. 该框架从图像中恢复深度,允许更安全的,指导的内手术.
科学领域:
- 医疗机器人 医疗机器人
- 计算机视觉 计算机视觉
- 生物医学成像技术 生物医学成像技术
背景情况:
- 三维 (3D) 感知对于光内机器人导航至关重要,需要准确的深度估计才能安全前进.
- 纤维束内镜为微型探针提供高分辨率,灵活的成像,但由于文物和缺乏地面真相数据,难以获得可靠的深度恢复.
研究的目的:
- 开发一个弱监督的框架,以增强光纤捆绑成像与深度估计能力.
- 为了实现精确的几何反,在没有配对深度标签或外部传感器的情况下,在流明范围内进行机器人导航.
主要方法:
- 采用了一种结合域翻译,伪深度监督和结构意识约束的新框架.
- 该网络仅使用原始图像进行训练,直接推断深度并重建光线拓.
- 该系统集成了磁性驱动,用于闭环控制.
主要成果:
- 该方法成功地从没有地面真相数据的光纤捆绑图像中推断出深度.
- 灯光拓重建为导航提供了倾斜角度指导.
- 有效的闭环导航在曲的光幻体中得到了证明.
结论:
- 可以通过弱监督学习实现深度启用纤维束内镜.
- 拟议的框架有助于现场机器人控制内手术的过程.
- 这一进步有可能提高微创手术机器人手术的安全性和有效性.
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