CAREER: Novel Geometric Techniques for Optimal Surface Detection in Medical Images

职业：医学图像中最佳表面检测的新几何技术Xiaodong Wu电子与计算机工程系放射肿瘤学系爱荷华大学自动检测体积图像中表示对象边界的全局最佳表面是基本的，并且在现代计算机辅助医学诊断和治疗以及其他重要的医学应用中仍然具有挑战性。 3D数据的分析通常在常规临床实践中以视觉方式进行，并且在逐切片的基础上以2D方式进行。提供3D数据的2D图像或定性体积渲染的最大强度投影也是常见的。然而，可靠的定量体积的3-D和4-D分割方法，是强大的，用户友好的，并适用于临床实践是不是常规可用。因此，在所采集的数据中可用的图像信息的重要且可能关键的部分可能不幸地从未用于临床诊断目的。这项研究涉及直接解决自动分割的需求，并将导致新的方法和实用的工具，大大加快了医学图像分析的步伐。调查研究的具体问题，最佳检测体积医学图像中的单个和多个相互作用的表面。具有高阶光滑度的曲面特别令人感兴趣。新引入的边缘加权几何图形表示的表面检测问题，允许将广泛的图像特征的制定，承认高度准确的分割结果。然而，它也带来了巨大的计算挑战。 目标几何问题包括高度光滑的表面识别，几何层划分，表面重建噪声数据，数字射线，约束k均值聚类，几何多重标记。计算几何技术在医学成像中的应用是智力上的深入，将导致计算几何和医学的进步。这项研究的一个重要目标是传播软件中实现的算法到医疗领域。在这样做的过程中，它可以帮助进一步将计算机科学与医学结合起来。除了它的研究影响，这个项目的教育目标是促进机构范围内的努力，以促进和促进生物医学研究的实力在爱荷华州的大学成功的跨学科本科和研究生课程的延伸。

CAREER: Novel Geometric Techniques for Optimal Surface Detection in Medical ImagesXiaodong WuDepartment of Electrical and Computer EngineeringDepartment of Radiation OncologyThe University of IowaAutomated detection of globally optimal surfaces representing object boundaries in volumetric images is fundamental and remains challenging in modern computer-assisted medical diagnosis and treatment as well as other important medical applications. The analyses of 3-D data are typically performed visually in routine clinical practice, and are done in 2-D on a slice-by-slice basis. Maximum intensity projections providing 2-D images of the 3-D data or qualitative volume renderings are also common. However, reliable quantitative volumetric 3-D and 4-D segmentation methods that are robust, user-friendly, and applicable to clinical practice are not routinely available. As such, a significant and possibly critical portion of the image information available in the acquired data may unfortunately be never used for clinical diagnostic purposes. This research involves directly addressing the needs of automated segmentation and will result in novel methodologies and practical tools that greatly accelerate the pace of medical image analysis. The investigator studies the specific problems of optimally detecting single and multiple interacting surfaces in volumetric medical images. Surfaces with high-order smoothness are of particular interest. The newly introduced edge-weighted geometric graph representation for the surface detection problems allows incorporating a wide spectrum of image features into the formulation, admitting highly accurate segmentation results. However, it also brings tremendous computational challenges. The target geometric problems include highly smooth surface identification, geometric layer partitioning, surface reconstruction from noisy data, digital rays, constrained k-means clustering, and geometric multiple labeling. The application of computational geometry techniques to medical imaging is intellectually deep and will result in advances in both computational geometry and medicine. An important goal of this research is dissemination of implemented algorithms in software to medical domains. In doing this, it can help to further integrate computer science with medicine. Beyond its research impact, the educational goal of this project is to facilitate the institution-wide effort to foster and promote the extension of the strength of biomedical research at the University of Iowa to successful interdisciplinary undergraduate and graduate programs.