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大脑解剖结构的自动化算法，对于发现异常脑模式、分析各种脑部疾病和研究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/计算框架来执行3D整体脑图像理解。该框架强调严谨、高效、有效的基于学习的统计模型，将大脑解剖结构的复杂外观、多变的3D形状和巨大的空间构形整合在一起，通过判别方法提取隐含模型，具有融合大量信息和快速获得决策的优势。显式模型通过产生式方法可以直观地表达信息，从而更好地解释结构和模型的转换和尺度变化。PI通过隐式模型和显式模型的结合，从几个方面探索了3D图像分析中判别模型和生成模型之间的协调关系：(1)具有丰富外观的基于学习的模型，以及隐含的形状和上下文；(2)将骨骼和表面相结合的3D形状；(3)有效的3D形状表示和相似性度量；(4)基于组件的同时配准和分割。本研究将有助于自动化提取大量解剖结构的过程，并增强检测大脑疾病、监测健康状况、研究药物效果和发现大脑功能所需的形状分析。该模型超越了医学图像分析的范畴，可应用于统计建模/计算、计算机视觉、机器学习中的多变量标注等问题。

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.