• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>IEEE International Symposium on Mixed and Augmented Reality >Computer-Assisted Laparoscopic myomectomy by augmenting the uterus with pre-operative MRI data
【24h】

Computer-Assisted Laparoscopic myomectomy by augmenting the uterus with pre-operative MRI data

机译：通过术前MRI数据扩大子宫来进行计算机辅助腹腔镜子宫肌瘤切除术

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

An active research objective in Computer Assisted Intervention (CAI) is to develop guidance systems to aid surgical teams in la-paroscopic Minimal Invasive Surgery (MIS) using Augmented Reality (AR). This involves registering and fusing additional data from other modalities and overlaying it onto the laparoscopic video in realtime. We present the first AR-based image guidance system for assisted myoma localisation in uterine laparosurgery. This involves a framework for semi-automatically registering a pre-operative Magnetic Resonance Image (MRI) to the laparoscopic video with a deformable model. Although there has been several previous works involving other organs, this is the first to tackle the uterus. Furthermore, whereas previous works perform registration between one or two laparoscopic images (which come from a stereo laparoscope) we show how to solve the problem using many images (e.g. 20 or more), and show that this can dramatically improve registration. Also unlike previous works, we show how to integrate occluding contours as registration cues. These cues provide powerful registration constraints and should be used wherever possible. We present retrospective qualitative results on a patient with two myomas and quantitative semi-synthetic results. Our multi-image framework is quite general and could be adapted to improve registration in other organs with other modalities such as CT.
机译：计算机辅助干预的积极研究目标（CAI）是制定指导系统，以帮助使用增强现实（AR）的La-Paroscopic最小侵入性手术（MIS）的手术团队。这涉及从其他方式注册和融合其他数据，并将其覆盖到腹腔镜视频中实时。我们介绍了第一个基于AR的图像引导系统，用于辅助肌瘤定位在子宫剖腹产。这涉及用可变形的模型进行半自动地将预次磁共振图像（MRI）注册到腹腔镜视频的框架。虽然有几个以前的作品涉及其他器官，但这是第一个解决子宫的作品。此外，而以前的作品在一个或两个腹腔镜图像（来自立体声腹腔镜）之间进行登记，我们展示了如何使用许多图像（例如20或更多）来解决问题，并显示这可以大大改善注册。同样与以前的作品不同，我们展示了如何将遮挡轮廓集成为注册线索。这些提示提供了强大的注册限制，应尽可能使用。我们向患者提出回顾性定性结果，具有两种Myomas和定量半合成结果。我们的多图像框架是一般的，可以适应改善其他器官的配音，例如CT等其他模态。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号