4D Multi object segmentation based on MR image sequences - Medical application for evaluation of myocardial differences in shape and function after infarction

基于 MR 图像序列的 4D 多对象分割 - 评估梗塞后心肌形状和功能差异的医学应用

基本信息

批准号：
263745607
负责人：
Dr. Jan Ehrhardt
金额：
--
依托单位：
Institut für Medizinische Biometrie und Epidemiologie
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2014
资助国家：
德国
起止时间：
2013-12-31 至 2020-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/263745607?language=en
关键词：
4D Multi object segmentation based

项目摘要

Myocardial infarction (MI) is the leading cause of death for both men and women worldwide in the western civilization. The quality of life and the course of disease for MI patients depend on the revitalization of the myocardium and avoiding the development of a persistent dysfunctional contraction of the heart, which can lead to progressive impairment of the heart function combined with cardiac remodeling. Early detection of patients with risk of remodeling is clinical relevant because effective therapies have to be initiated early to avoid remodeling.The aim of this work is to develop new automatic methods for detection, quantification and prediction of myocardial remodeling based on spatio temporal MRI datasets. Therefore, a new workflow has to be developed that enables automatic pre-processing of the datasets, a model based segmentation and motion field estimation in 4D MRI as well as quantitative parameter extraction, analysis and visualization. For development and evaluation a comprehensive data pool of baseline and follow-up MRI dataset of MI patients exists. All datasets were acquired with standard imaging parameters. MRI data of healthy subjects with high temporal and/or spatial resolution exists as well. Overall more than 360 anonymized MRI datasets are available for this project. They contain multiple MRI sequences (e.g. Cine-MRI, LGE-MRI, T2w-MRI) and manual segmentations of the relevant cardiac structures.A central aspect of this work is the development of a model based approach. This method uses a new integrated segmentation and motion estimation of the left and right ventricle approach and considers knowledge about shape and shape variations as well as the typical motion of the heart. Another aspect is the quantitative analysis and classification of clinical MRI datasets. Here, learning based classification algorithms are used. Therefore, relevant parameters characterizing shape and motion of the heart are extracted and analyzed providing an automatic detection and prognosis of myocardial remodeling.

心肌梗塞（MI）是西方文明中全世界男性和女性死亡的主要原因。 MI 患者的生活质量和病程取决于心肌的再生和避免心脏持续功能失调的发展，这可能导致心功能进行性损害并伴有心脏重构。早期检测有重构风险的患者具有临床意义，因为必须尽早开始有效的治疗以避免重构。这项工作的目的是开发基于时空 MRI 数据集的心肌重构检测、量化和预测的新自动方法。因此，必须开发一种新的工作流程，实现数据集的自动预处理、4D MRI 中基于模型的分割和运动场估计以及定量参数提取、分析和可视化。为了开发和评估，存在 MI 患者基线和随访 MRI 数据集的综合数据库。所有数据集均使用标准成像参数获取。还存在具有高时间和/或空间分辨率的健康受试者的 MRI 数据。总共有超过 360 个匿名 MRI 数据集可供该项目使用。它们包含多个 MRI 序列（例如 Cine-MRI、LGE-MRI、T2w-MRI）和相关心脏结构的手动分割。这项工作的一个核心方面是开发基于模型的方法。该方法使用新的左心室和右心室集成分割和运动估计方法，并考虑有关形状和形状变化以及心脏典型运动的知识。另一个方面是临床MRI数据集的定量分析和分类。这里，使用基于学习的分类算法。因此，提取并分析表征心脏形状和运动的相关参数，提供心肌重塑的自动检测和预后。