INTRAOPERATIVE IMAGE GUIDED NEUROSURGERY DVMT

术中影像引导神经外科 DVMT

• 批准号: 6123562
• 负责人: William Eric Grimson
• 金额: $ 1.68万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-09-30 至 1999-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6123562
• 关键词: bioengineering /biomedical engineering; biomedical resource; nervous system; surgery; technology /technique

To support the goals of image guided neurosurgery, we plan to continue our work in developing novel computer vision systems that: segment medical scans into graphical models of distinct anatomical structures; register such models with the position of the patient in the operating room; provide visualization tools that let the surgeon see the full panoply of the patient's anatomy while at the same time supporting surgical access through minimally invasive openings; track surgical instruments relative to the patient and provide the surgeon with a visualization of the position of the instrument with respect to the segmented scans; and automatically adjust models to reflect deformations and changes as the surgery proceeds. In the area of segmentation, we will continue to develop clinically relevant methods for automatically or semi-automatically constructing anatomical models from standard medical scans. The goal is to build patient- specific models that directly highlight structures of interest to the surgeon. In a related sub-proposal, we provide a detailed description of novel methods to be developed to support such segmentation techniques. Within the context of this sub-proposal, we intend to: Extend our current work in segmentation by coupling tissue classification methods with anatomical atlases. This will allow us to use predefined models to serve as templates for guiding the extraction of structures from scans Evaluate the accuracy and stability of existing and modified segmentation techniques. This will include baseline studies that compare our segmentation results against those of expert radiologists, and will include verification of the accuracy of our segmented results against actual anatomy observed during the surgical procedure. Create automatic tools that directly support Image Guided Surgery in neurosurgery, by bringing the segmented models into direct relationship with surgical patients, for use in guidance and navigation. This will include evaluation by the surgeon of what aspect of segmented models are more directly relevant and useful in the surgical procedure.

为了支持图像引导神经外科的目标，我们计划 继续我们开发新型计算机视觉系统的工作： 分段医学扫描中的图形模型不同的解剖学模型 结构；在患者的位置注册此类模型 手术室；提供可视化工具使外科医生 同时查看患者解剖结构的全套 通过微创开口支持手术手术；追踪 相对于患者的手术器械并为外科医生提供 与仪器位置相对于 分段扫描；并自动调整模型以反映 随着手术的进行，变形和变化。 在细分领域，我们将继续在临床上发展 自动或半自动构建的相关方法 来自标准医学扫描的解剖模型。 目标是建造 患者 - 特定模型，直接突出显示的结构 外科医生的兴趣。 在相关的子序言中，我们提供了 详细描述要开发的新方法来支持这种 细分技术。 在这个子峰的背景下，我们 打算： 通过耦合组织扩展我们目前的分割工作 用解剖图谱的分类方法。 这将使我们能够 使用预定义的模型作为指导提取的模板 扫描结构 评估现有和修改的准确性和稳定性 细分技术。 这将包括基线研究 将我们的细分结果与专家放射科医生的结果进行比较， 并将包括验证我们分割结果的准确性 反对手术过程中观察到的实际解剖结构。 创建自动工具，直接支持图像指导手术 神经外科，通过将分段模型直接带入 与手术患者的关系，用于指导和 导航。 这将包括外科医生的评估 分段模型的方面更直接相关和有用 外科手术。