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使用基于学习的单眼vSLAM模型进行密集表面重建,用于腹腔镜手术.

James Yu1,2,3, Kelden Pruitt1,3, Nati Nawawithan1,3

  • 1Center for Imaging and Surgical Innovation, University of Texas at Dallas, Richardson, TX.

Proceedings of SPIE--the International Society for Optical Engineering
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PubMed
概括
此摘要是机器生成的。

本研究介绍了一种深度学习的视觉同时定位和映射 (vSLAM) 算法,用于3D手术场景重建. 这种方法增强了增强现实 (AR) 手术,通过从单眼腹腔镜提供准确的摄像头姿势估计,而无需额外的传感器.

关键词:
3D重建重建的3D重建这就是为什么MRI是MRI.斯拉姆斯兰姆斯兰姆斯兰姆斯兰姆斯兰姆斯兰姆斯兰姆斯兰姆斯兰姆斯增强现实 (AR) 是一种增强现实.深度学习是一种深度学习.图像指导手术是指导图像的手术.腹腔镜检查是指腹腔镜检查.神经网络的神经网络的神经网络

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科学领域:

  • 医疗成像医学成像
  • 计算机视觉 计算机视觉
  • 手术技术 手术技术

背景情况:

  • 增强现实 (AR) 在外科手术中越来越多地用于将手术前成像叠加到手术场上.
  • 准确地记录可变形解剖学,特别是在微创手术中使用单眼腹腔镜,仍然是一个挑战.
  • 强大的手术场景重建对于精确的AR指导程序和相关应用至关重要.

研究的目的:

  • 开发和评估基于深度学习的视觉同步定位和映射 (vSLAM) 算法,用于3D手术场景重建.
  • 为了使准确的摄像头姿势估计和密集的3D重建从单眼腹腔镜视频数据.
  • 为在具有挑战性的外科表面上评估vSLAM算法建立一个框架.

主要方法:

  • 使用最先进的深度学习vSLAM算法来处理单眼腹腔镜视频.
  • 在实时中生成密集的3D重建,摄像头姿势估计和深度地图.
  • 开发了一个框架来评估vSLAM在非兰伯特式,低质感的外科表面上的性能.

主要成果:

  • 该vSLAM方法成功地产生了强大的3D重建和准确的摄像头姿势估计从单眼腹腔镜视频.
  • 该系统提供了这些关键输出,而不需要立体视觉或额外的传感器,提高了可用性.
  • 证明了评估框架在手术环境中评估vSLAM算法的实用性.

结论:

  • 拟议的深度学习vSLAM方法为使用单眼腹腔镜准确的3D手术场景重建提供了可行的解决方案.
  • 这种方法改善了先进的AR导向手术,远程辅助和外科模拟的基础.
  • 开发的评估方法将有助于改进未来的手术应用的vSLAM算法.