A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging
A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging
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DOI：
10.1016/j.media.2020.101832
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发表时间：
2021-01-01
影响因子：
10.9
通讯作者：
Zhao, Jichao
中科院分区：
文献类型：
--
作者：
Xiong, Zhaohan;Xia, Qing;Zhao, Jichao
医学图像的分割，特别是用于可视化病变心房结构的晚期钆增强磁共振成像（LGE-MRI），是房颤消融治疗的关键第一步。然而，由于造影剂引起的强度变化，LGE-MRI的直接分割具有挑战性。由于大多数临床研究都依赖于人工，劳动密集型方法，因此自动方法非常有趣，特别是优化的机器学习方法。为了解决这个问题，我们组织了2018年左心房分割挑战赛，使用了154个3D LGE-MRI，目前世界上最大的心房LGE-MRI数据集，以及由三位医学专家分割的左心房的相关标签，最终吸引了27个国际团队的参与。在本文中，通过进行亚组分析和进行超参数分析，使用技术和生物指标对提交的算法进行了广泛的分析，提供了卷积神经网络（CNN）的主要设计选择的全貌以及实现最先进的左心房分割的实际考虑因素。结果表明，顶级方法实现了93.2%的Dice得分和0.7 mm的平均表面到表面距离，显著优于现有技术。特别是，我们的分析表明，双顺序使用CNN，其中第一个CNN用于自动感兴趣区域定位，随后的CNN用于精细区域分割，实现了比传统方法和包含单个CNN的机器学习方法更优越的上级结果。这项大规模的基准研究朝着心房LGE-MRI分割方法的改进迈出了重要的一步，并将作为评估和比较该领域未来工作的重要基准。此外，这项研究的结果可能会扩展到其他成像数据集和模式，对更广泛的医学成像界产生影响。(C)2020爱思唯尔B. V.保留所有权利。
Segmentation of medical images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) used for visualizing diseased atrial structures, is a crucial first step for ablation treatment of atrial fibrillation. However, direct segmentation of LGE-MRIs is challenging due to the varying intensities caused by contrast agents. Since most clinical studies have relied on manual, 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 atrial 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 overall picture of the major design choices of convolutional neural networks (CNNs) and practical considerations for achieving state-of-the-art left atrium segmentation. Results show that the top method achieved a Dice score of 93.2% and a mean surface to surface distance of 0.7 mm, significantly outperforming prior state-of-the-art. Particularly, our analysis demonstrated that double sequentially used CNNs, in which a first CNN is used for automatic region-of-interest localization and a subsequent CNN is used for refined regional segmentation, achieved superior results than traditional methods and machine learning approaches containing single CNNs. This large-scale benchmarking study makes a significant step towards much-improved segmentation methods for atrial LGE-MRIs, and will serve as an important benchmark for evaluating and comparing the future works in the field. Furthermore, the findings from this study can potentially be extended to other imaging datasets and modalities, having an impact on the wider medical imaging community. (C) 2020 Elsevier B.V. All rights reserved.