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使用卷积神经网络进行 laparoscopic 手术实时工具定位.

Diego Benavides1, Ana Cisnal1, Carlos Fontúrbel1

  • 1Instituto de las Tecnologías Avanzadas de la Producción (ITAP), Escuela de Ingenierías Industriales, Universidad de Valladolid, Paseo Prado de la Magdalena 3-5, 47011 Valladolid, Spain.

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概括
此摘要是机器生成的。

这项研究引入了一个卷积神经网络模型,用于实时在腹腔镜中进行手术工具定位. 基于砂表的模型通过准确识别工具来增强手术机器人,提高效率.

关键词:
人工智能的人工智能是人工智能.生物医学图像处理卷积神经网络是一种卷积神经网络.腹腔镜 机器人手术 手术机器人手术实时实时的时间.手术工具追踪器 手术工具追踪器

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科学领域:

  • 医疗机器人 医疗机器人
  • 计算机视觉 计算机视觉
  • 手术技术 手术技术

背景情况:

  • 部分自动化机器人系统对于提高手术精度和效率至关重要.
  • 腹腔镜手术需要精确的仪器控制,往往需要显著的手工灵巧.

研究的目的:

  • 利用卷积神经网络开发用于腹腔镜手术的实时工具定位方法.
  • 为了使最多两种手术工具的同时定位.

主要方法:

  • 设计了一个卷积神经网络模型,其中包括两个连续的沙钟模块.
  • 该模型在三个数据集上进行了训练和评估:ITAP,Atlas Dione和EndoVis挑战.
  • 用Grad-CAM技术来确定模型的可解释性.

主要成果:

  • 最好的基于砂表的模型实现了92.86%的精度和27.64 FPS,适合机器人集成.
  • 一个独立的测试组评估显示,准确性略有降低,这表明概括性有限.
  • 该模型通过Grad-CAM分析证明了功能洞察力.

结论:

  • 拟议的模型为自动化腹腔镜手术任务提供了一个有希望的方法.
  • 实时工具定位可以通过减少手动内镜操纵来提高手术效率.
  • 需要进一步开发以提高更广泛的临床应用的通用性.