Experimental Partnership - Real-Time Computer Vision Based Spatial Mapping and Referencing for Minimally Invasive Surgery

实验合作伙伴 - 基于实时计算机视觉的微创手术空间测绘和参考

• 批准号: 0000417
• 负责人: Badrinath Roysam
• 金额: $ 130万
• 依托单位: Rensselaer Polytechnic Institute
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-10-01 至 2006-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0000417&HistoricalAwards=false
• 关键词: Experimental; Partnership; Real; Time; Computer

EIA-0000417Badrinath RoysamRensselaer Polytechnic InstituteExperimental Partnership-Real Time Computer Vision Based Spatial Mapping and Referencing for Minimally Invasive SurgeryThe goal is to develop core computer vision technology for a new generation of highly capable instruments for minimally invasive surgery. The specific applications are in opthamology. Developing the technology requires facing and overcoming several fundamental barriers that have plague computer vision systems. These include poor quality image data during surgery, difficult to model biological tissues, large scale and unpredictable motions of the eye, a need for extreme accuracy over long duration, a need for predictable response in a real-time implementation, and a need for transparency of the system as a whole.The computer vision technology will distinguish these instruments by making them "spatially-aware". This means they include capabilities for spatially mapping and spatial referencing. Spatial mapping is the problem of building and maintaining a seamless, wide-area map (mosaic) of the three-dimensional region of surgical interest and its surrounding. This map will be constructed from images acquired during diagnostic exploration and forms the basis for absolute and hence verifiable spatial referencing.Novel aspects of the work in mapping include: (1) a fast, recursive technique for tracing elongated structures and then detecting their branching and cross-over points, (2) a feature-based image-to-image matching algorithm that used a high-order transformation model and a hierarchy of robust estimation techniques, (3) a joint optimization of all image-to-mosaic transformations simultaneously, and (4) a 3-D reconstruction of the retinal surface despite an inherent inability to calibrate the imaging system. These techniques are each useful in other domains besides ophthalmology.

EIA-0000417 Badrinath RoysamRensselaer Polytechnic InstituteExperimental Partnership-Real Time Computer Vision Based Spatial Mapping and Referencing for Minimally Invasive Surgery的目标是为新一代高性能微创手术仪器开发核心计算机视觉技术。 具体的应用是在眼科。 开发该技术需要面对并克服困扰计算机视觉系统的几个基本障碍。 这些问题包括手术过程中图像数据质量差、生物组织建模困难、眼睛的大规模和不可预测的运动、需要长时间保持极高的精度、需要实时实现可预测的响应以及需要整个系统的透明性。计算机视觉技术将通过使这些仪器具有“空间感知”来区分它们。 这意味着它们包括空间映射和空间参考的能力。 空间映射是建立和维护手术感兴趣的三维区域及其周围的无缝广域地图（马赛克）的问题。 该地图将根据诊断勘探期间获得的图像构建，并形成绝对和可验证的空间参考的基础。（1）用于跟踪细长结构并随后检测其分支和交叉点的快速递归技术，（2）基于特征的图像到图像匹配算法，其使用高阶变换模型和鲁棒估计技术的层级，（3）同时对所有图像到马赛克的变换进行联合优化，以及（4）尽管固有地不能校准成像系统，但视网膜表面的3-D重建。 这些技术在除眼科学之外的其他领域都是有用的。