Shape Exploration for Medical Applications --- From Representation, Correspondence, Deformation to Image Segmentation

项目概述(宋旺)EIA-0312861宋旺南加州大学研究基金会本研究旨在开发一个完整的、实用的形状建模框架，并使用它来实现更可靠的医学图像分割。这项研究的动机是可靠地提取、表示和合并一些先验形状信息，可以极大地减少图像噪声造成的分割误差。为了有效地探索医学图像分割中的形状信息，将解决并整合三个重要问题：形状表示、形状对应和形状变形。形状表示是用一组地标点来简洁地描述一个形状。形状对应是在一组形状样本之间建立基于地标的对应。形状变形是首先构造一个形状模板，该模板捕捉特定对象类的典型形状特征，然后用它来从新图像中勾画出同一类对象。智力优势：本研究从一个独特的系统角度研究造型方法。虽然以前的研究通常集中在形状建模的特定问题上，但我们将在一个完整而统一的框架内研究所有三个相互关联的关键问题，即形状表示、形状对应和形状变形，特别是针对医学图像分割。在这个统一的框架下，我们开发了新的方法来解决这三个问题中最重要的问题。在形状表示中，我们将结合与分辨率无关的特征提取方法来更准确地估计关键形状参数，如位置、切线方向和曲率。这将在描述一组形状实例时提供准确、紧凑和灵活的表示格式。在形状匹配中，我们将开发全局和局部匹配方法来选择地标：(1)形状表示简单，(2)表示误差小，(3)形状间地标匹配最优。将使用先进的网络流技术将这三种措施整合为一个新的统一公式。在形状变形方面，我们将基于一组匹配良好的训练样本来开发有效的形状学习技术。与简单的高斯分布相比，这些技术使用了更通用和更准确的概率分布模型。在这些模型的基础上，我们将进一步发展我们的形状变形方法，以处理算法的健壮性和拓扑保持等重要问题。广泛的影响：医学影像产业的快速发展为我们提供了MRI、CT、PET、冷冻、显微等多种形式的海量数据，迫切需要发展先进的信息技术将这些医学数据转化为有用的信息。其中，几何形状信息因其在临床诊断中的广泛应用而显得尤为重要。这项研究将极大地促进医学图像中形状信息的挖掘，并为医生和放射科医生提供新的强大的计算工具。从长远来看，这项研究将进一步加强目前弥合信息技术与医疗应用之间差距的努力。此外，建议的研究还可以应用于许多其他应用，如视频跟踪和生物识别，这些应用与从医学图像进行形状探索具有相同的问题。这项研究也提供了许多教育贡献。我们将把医学图像处理整合到南卡罗来纳大学的一系列图像处理和计算机视觉课程中。这项研究还将通过协助授予该大学的NSF桥梁工程教育计划以及参加南卡罗来纳州州长科学与数学学院的暑期导师计划，积极为该州的K-12教育做出贡献。生物医学工程被选为南加州大学三个主要研究领域之一。因此，我们的研究工作将直接为全校的努力做出贡献。特别是，我们的努力可以与心理学系和新的结肠癌研究中心的神经成像倡议相结合。

Project Summary (Song Wang) EIA-0312861Song WhangUSC Research Foundation The proposed research is to develop an integrated and practical framework for shape modeling and use it to achieve more reliable segmentation of medical images. This research is motivated by the fact that reliable extraction, representation, and incorporation of some prior shape information can greatly reduce the segmentation error resulting from image noise. Three important problems will be addressed and integrated to effectively explore shape information for medical image segmentation: shape representation, shape correspondence, and shape deformation. The shape representation is to compactly describe a shape in terms of a set of landmark points. The shape correspondence is to establish landmark-based correspondence among a set of shape samples. The shape deformation is to first construct a shape template that captures representative shape characteristics of a particular object class, and then use it to delineate an object of the same class from a new image. Intellectual Merits: This research studies the shape modeling method from a unique systematic perspective. While previous researches usually focus on a particular problem in shape modeling, we will investigate all three interrelated key problems, i.e., shape representation, shape correspondence, and shape deformation, in an integrated and unified framework, specifically for medical image segmentation. Under this unified framework, we develop novel methods to address most important issues in each of these three problems. In the shape representation, we will incorporate our resolution-independent feature extraction method to estimate key shape parameters such as position, tangent direction, and curvature more accurately. This will provide a representation format that is accurate, compact, and flexible in describing a group of shape instances. In the shape correspondence, we will develop global and local correspondence methods to choose landmarks based on the following criteria: (1) simple shape representation, (2) small representation error, and (3) optimal inter-shape landmark correspondence. An advanced network-flow technique will be used to integrate these three measures into a new unified formulation. In the shape deformation, we will develop effective shape-learning techniques based on a group of well-corresponded training samples. The techniques employ more general and accurate probability distribution models than simple Gaussian distributions. Based on the models, we will further develop our shape deformation method to deal with important issues like algorithm robustness and topology preservation. Broad Impacts: Fast growth of medical imaging industry provides us tremendous amount of data in many forms like MRI, CT, PET, cryosection, microscopic, etc. It is urgent to develop advanced information technology to convert those medical data into useful information. Among them, geometric shape information is of particular importance as they are widely used in clinical diagnoses. The proposed research will greatly facilitate the shape information exploration from medical images and provide physician and radiologists new and powerful computational tools. In the long term, this research will further intensify the current efforts to bridge the gap between information technology and medical applications. Furthermore, the proposed research can be applied to many other applications like video tracking and biometrics that shares the same problems as in shape exploration from medical images. This research also provides many educational contributions. We will integrate the medical image processing into a series of image processing and computer vision courses in the University of South Carolina (USC). This research will also actively contribute to the K-12 education in the state by assisting the NSF Bridges for Engineering Education Program awarded to the University and participating in the summer mentor program by the South Carolina Governor's School for Science and Mathematics. Biomedical engineering has been selected as one of three main focus areas for research at USC. Thus, our research effort will directly contribute to the university wide efforts. In particular, our effort can be integrated with the neuro-imaging initiative at the Psychology Department and the new Center for Colon Cancer Research.