Deep Learning Segmentation of the Right Ventricle in Cardiac MRI: The M&Ms Challenge
Deep Learning Segmentation of the Right Ventricle in Cardiac MRI: The M&Ms Challenge
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DOI：
10.1109/jbhi.2023.3267857
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发表时间：
2023-07-01
影响因子：
7.7
通讯作者：
Lekadir, Karim
中科院分区：
文献类型：
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作者：
Martin-Isla, Carlos;Campello, Victor M.;Lekadir, Karim
近年来，已经提出了几种深度学习模型来准确量化和诊断心脏病理。这些自动化工具严重依赖于MRI图像中心脏结构的准确分割。然而，右心室的分割是具有挑战性的，由于其高度复杂的形状和边界不明确。因此，需要新的方法来处理这种结构的几何和纹理复杂性，特别是在存在诸如右心房扩张、三尖瓣返流、心律失常、法洛四联症和心房间沟通的病理的情况下。上一次关于右心室分割的MICCAI挑战于2012年举行，仅包括来自单个临床中心的48例病例。作为第12届心脏统计数据库和计算模型研讨会（STACOM 2021）的一部分，组织M&MS-2挑战赛旨在促进研究界对多疾病、多视图和多中心心脏MRI中右心室分割的兴趣。使用来自三家不同供应商的九种不同扫描仪从三家西班牙医院收集了360例CMR病例，包括短轴和长轴4腔视图，并包括一组不同的右心室和左心室病理。参与者提供的解决方案表明，nnU-Net总体上取得了最佳效果。然而，多视图方法能够捕获额外的信息，强调需要整合多种心脏疾病、视图、扫描仪和采集协议，以产生可靠的自动心脏分割算法。
In recent years, several deep learning models have been proposed to accurately quantify and diagnose cardiac pathologies. These automated tools heavily rely on the accurate segmentation of cardiac structures in MRI images. However, segmentation of the right ventricle is challenging due to its highly complex shape and ill-defined borders. Hence, there is a need for new methods to handle such structure's geometrical and textural complexities, notably in the presence of pathologies such as Dilated Right Ventricle, Tricuspid Regurgitation, Arrhythmogenesis, Tetralogy of Fallot, and Inter-atrial Communication. The last MICCAI challenge on right ventricle segmentation was held in 2012 and included only 48 cases from a single clinical center. As part of the 12th Workshop on Statistical Atlases and Computational Models of the Heart (STACOM 2021), the M&Ms-2 challenge was organized to promote the interest of the research community around right ventricle segmentation in multi-disease, multi-view, and multi-center cardiac MRI. Three hundred sixty CMR cases, including short-axis and long-axis 4-chamber views, were collected from three Spanish hospitals using nine different scanners from three different vendors, and included a diverse set of right and left ventricle pathologies. The solutions provided by the participants show that nnU-Net achieved the best results overall. However, multi-view approaches were able to capture additional information, highlighting the need to integrate multiple cardiac diseases, views, scanners, and acquisition protocols to produce reliable automatic cardiac segmentation algorithms.