Zhuang Xiahai, Xu Jiahang, Luo Xinzhe, Chen Chen, Ouyang Cheng, Rueckert Daniel, Campello Victor M, Lekadir Karim, Vesal Sulaiman, RaviKumar Nishant, Liu Yashu, Luo Gongning, Chen Jingkun, Li Hongwei, Ly Buntheng, Sermesant Maxime, Roth Holger, Zhu Wentao, Wang Jiexiang, Ding Xinghao, Wang Xinyue, Yang Sen, Li Lei
School of Data Science, Fudan University, Shanghai, China. Electronic address: https://www.sdspeople.fudan.edu.cn/zhuangxiahai/?
School of Data Science, Fudan University, Shanghai, China.
Med Image Anal. 2022 Oct;81:102528. doi: 10.1016/j.media.2022.102528. Epub 2022 Jul 9.
Accurate computing, analysis and modeling of the ventricles and myocardium from medical images are important, especially in the diagnosis and treatment management for patients suffering from myocardial infarction (MI). Late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) provides an important protocol to visualize MI. However, compared with the other sequences LGE CMR images with gold standard labels are particularly limited. This paper presents the selective results from the Multi-Sequence Cardiac MR (MS-CMR) Segmentation challenge, in conjunction with MICCAI 2019. The challenge offered a data set of paired MS-CMR images, including auxiliary CMR sequences as well as LGE CMR, from 45 patients who underwent cardiomyopathy. It was aimed to develop new algorithms, as well as benchmark existing ones for LGE CMR segmentation focusing on myocardial wall of the left ventricle and blood cavity of the two ventricles. In addition, the paired MS-CMR images could enable algorithms to combine the complementary information from the other sequences for the ventricle segmentation of LGE CMR. Nine representative works were selected for evaluation and comparisons, among which three methods are unsupervised domain adaptation (UDA) methods and the other six are supervised. The results showed that the average performance of the nine methods was comparable to the inter-observer variations. Particularly, the top-ranking algorithms from both the supervised and UDA methods could generate reliable and robust segmentation results. The success of these methods was mainly attributed to the inclusion of the auxiliary sequences from the MS-CMR images, which provide important label information for the training of deep neural networks. The challenge continues as an ongoing resource, and the gold standard segmentation as well as the MS-CMR images of both the training and test data are available upon registration via its homepage (www.sdspeople.fudan.edu.cn/zhuangxiahai/0/mscmrseg/).
从医学图像中准确计算、分析和建模心室与心肌非常重要，尤其对于患有心肌梗死（MI）的患者的诊断和治疗管理而言。延迟钆增强（LGE）心脏磁共振成像（CMR）为可视化心肌梗死提供了一项重要方案。然而，与其他序列相比，带有金标准标签的LGE CMR图像特别有限。本文展示了与2019年医学图像计算方法国际会议（MICCAI）联合举办的多序列心脏磁共振（MS-CMR）分割挑战赛的精选结果。该挑战赛提供了一组配对的MS-CMR图像数据集，包括来自45例患有心肌病患者的辅助CMR序列以及LGE CMR图像。其目的是开发新算法，并为专注于左心室心肌壁和两个心室血腔的LGE CMR分割对现有算法进行基准测试。此外，配对的MS-CMR图像能够使算法结合来自其他序列的互补信息用于LGE CMR的心室分割。选择了九项具有代表性的作品进行评估和比较，其中三种方法是无监督域适应（UDA）方法，另外六种是有监督方法。结果表明，这九种方法的平均性能与观察者间的差异相当。特别是，来自有监督方法和UDA方法的排名靠前的算法都能生成可靠且稳健的分割结果。这些方法的成功主要归功于包含了来自MS-CMR图像的辅助序列，这些序列为深度神经网络的训练提供了重要的标签信息。该挑战赛作为一项持续可用的资源仍在继续，通过其主页（www.sdspeople.fudan.edu.cn/zhuangxiahai/0/mscmrseg/）注册后可获取训练和测试数据的金标准分割以及MS-CMR图像。
