CAREER: Holistic 3D Brain Image Parsing by Integrating Implicit and Explicit Models

职业：通过集成隐式和显式模型进行整体 3D 大脑图像解析

• 批准号: 0844566
• 负责人: Zhuowen Tu
• 金额: $ 46万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-07-01 至 2013-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0844566&HistoricalAwards=false
• 关键词: CAREER; Holistic; 3D; Brain; Image

Designing automated algorithms to extract and analyze anatomical brain structures from neuro-images is of significant scientific and clinical importance in detecting abnormal brain patterns, analyzing various brain diseases, and studying the brain growth.This project will develop a general statistical modeling/computing framework to perform 3D holistic brain image understanding. The framework emphasizes rigorous, efficient, and effective learning-based statistical models to integrate the complex appearances, varying 3D shapes, and the large spatial configuration of anatomical brain structures.Implicit models through discriminative approaches have the advantages of fusing a large amount of information and obtaining decisions quickly. Explicit models through generative approaches can directly represent the information and thus, better explain the structure and model the transformation and scale change. The PI explores harmonic relationships between discriminative and generative models for 3D image parsing by combining implicit and explicit models along several directions: (1) learning-based models with rich appearance, and implicit shape and context; (2) integrating skeleton with surfaces for 3D shapes; (3) effective 3D shape representation and similarity measure; (4) component-based simultaneous registration and segmentation.This research will contribute to automating the process of extracting a large number of anatomical structures, and enhancing the shape analysis needed for detecting brain diseases, monitoring health conditions, studying drug effects, and discovering brain functions. The scope of the proposed model goes beyond medical image analysis and can be applied in other problems of statistical modeling/computing, computer vision, multi-variate labeling in machine learning.

设计自动化算法从神经图像中提取和分析大脑解剖结构，对于检测异常大脑模式、分析各种大脑疾病和研究大脑发育具有重要的科学和临床意义。本项目将开发一个通用的统计建模/计算框架，以实现3D整体大脑图像理解。 该框架强调严格、高效、有效的基于学习的统计模型，以整合复杂的外观、多变的3D形状和解剖大脑结构的大空间配置，通过判别方法的隐式模型具有融合大量信息和快速获得决策的优点。通过生成方法的显式模型可以直接表示信息，从而更好地解释结构并模拟转换和尺度变化。PI通过将隐式和显式模型沿着几个方向相结合来探索3D图像解析的判别模型和生成模型之间的和谐关系：（1）具有丰富外观的基于学习的模型，以及隐式形状和上下文;（2）将3D形状的骨架与表面集成;（3）有效的3D形状表示和相似性度量;（4）基于组件的同步配准和分割。该研究将有助于自动提取大量解剖结构的过程，并增强检测脑部疾病，监测健康状况，研究药物作用所需的形状分析，和发现大脑功能。所提出的模型的范围超出了医学图像分析，可以应用于统计建模/计算，计算机视觉，机器学习中的多变量标记等问题。