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

设计从神经图像中提取和分析脑解剖结构的自动化算法，对于检测异常脑模式、分析各种脑疾病和研究脑生长具有重要的科学和临床意义。该项目将开发一个通用的统计建模/计算框架来执行3D整体脑图像理解。该框架强调严谨、高效和有效的基于学习的统计模型，以整合大脑解剖结构的复杂外观、变化的3D形状和大空间配置。通过判别方法建立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和建模转换和规模变化。PI通过将隐式和显式模型相结合，从以下几个方面探索了三维图像解析中判别模型和生成模型之间的和谐关系：(1)基于学习的模型，具有丰富的外观，隐含的形状和上下文；(2)将骨架与曲面结合，实现三维形状；(3)有效的三维形状表示和相似性度量；(4)基于分量的同步配准与分割。这项研究将有助于自动化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、研究药物效应和发现大脑功能所需的形状分析。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其他问题。

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.