LV quantification 2019 datasets

该挑战是左心室全定量挑战MICCAI 2018 (LVQuan18) 的扩展，主要区别在于该挑战 (LVQuan19) 将提供原始数据，而无需对训练和测试阶段进行预处理，这比LVQuan18提供的数据更具临床意义。该挑战解决了接受心脏MRI的患者左心室 (LV) 的分析。

为了评估心脏的功能，LV的功能，形态和时间动力学具有临床意义。临床常规中的这种分析很耗时，并且容易出错和观察者之间的差异。在此挑战中，将进行LV腔和心肌的提取，然后进行区域壁厚，LV尺寸和心动周期阶段的分类的回归。这些是评估LV功能的常见且重要的参数。

这项挑战的目的是学习有效的机器学习模型，该模型可以直接从MR图像估计一组临床重要的LV指数 (区域壁厚，腔尺寸，腔和心肌面积，心脏相)。在整个过程中不需要中间分段。

This challenge is an extension of the Left Ventricle Full Quantification Challenge MICCAI 2018 (LVQuan18). The key distinction is that this challenge (LVQuan19) provides raw imaging data without requiring preprocessing for both training and test phases, which is more clinically relevant than the data released in LVQuan18. This challenge focuses on the analysis of the left ventricle (LV) in patients who underwent cardiac magnetic resonance imaging (MRI) scans.

To evaluate cardiac function, the LV's function, morphology, and temporal dynamics hold significant clinical value. Such analyses performed in clinical routine are time-consuming, prone to errors, and exhibit considerable inter-observer variability. In this challenge, participants will carry out extraction of the LV cavity and myocardium, followed by regression for regional wall thickness and LV dimensions, as well as classification of cardiac cycle phases. These are common and critical parameters for assessing LV function.

The objective of this challenge is to develop effective machine learning models that can directly estimate a set of clinically important LV indices (regional wall thickness, cavity dimensions, cavity and myocardial area, cardiac phases) from MR images, with no intermediate segmentation steps required throughout the entire processing pipeline.

搜集汇总

背景与挑战

背景概述

LV quantification 2019 datasets是MICCAI 2018左心室全定量挑战的扩展版本，提供原始数据用于直接从MR图像估计左心室临床指标。该数据集由耶鲁大学医学院发布，旨在通过机器学习模型简化左心室功能评估流程。