2018LA_Seg.zip

Segmentation of cardiac images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) widely used for visualizing diseased cardiac structures, is a crucial first step for clinical diagnosis and treatment. However, direct segmentation of LGE-MRIs is challenging due to its attenuated contrast. Since most clinical studies have relied on manual and labor-intensive approaches, automatic methods are of high interest, particularly optimized machine learning approaches. To address this, we organized the "2018 Left Atrium Segmentation Challenge" using 154 3D LGE-MRIs, currently the world s largest cardiac LGE-MRI dataset, and associated labels of the left atrium segmented by three medical experts, ultimately attracting the participation of 27 international teams. In this paper, extensive analysis of the submitted algorithms using technical and biological metrics was performed by undergoing subgroup analysis and conducting hyper-parameter analysis, offering an over

心脏图像的分割，特别是广泛用于显示病变心脏结构的延迟增强磁共振成像（LGE-MRI），是临床诊断和治疗的关键第一步。然而，由于LGE-MRI的对比度衰减，其直接分割具有挑战性。鉴于大多数临床研究依赖于手动且劳动密集型的方法，自动方法，尤其是优化的机器学习方法，备受关注。为此，我们组织了“2018左侧心房分割挑战赛”，使用154张3D LGE-MRI图像，目前这是世界上最大的心脏LGE-MRI数据集，并附有三位医学专家分割的左侧心房标签，最终吸引了27支国际团队的参与。本文对提交的算法进行了广泛的技术和生物学指标分析，通过子组分析和超参数分析，提供了对算法性能的深入理解。

搜集汇总

背景与挑战

背景概述

该数据集是2018年左心房分割挑战赛使用的数据，包含154个3D LGE-MRI图像及其专家标注的左心房标签，是目前全球最大的心脏LGE-MRI数据集。它旨在推动自动分割方法的发展，通过挑战赛吸引了27个国际团队参与，最佳方法达到了93.2%的dice分数，为心脏图像分割领域提供了重要基准。