Evaluation of current algorithms for segmentation of scar tissue from late gadolinium enhancement cardiovascular magnetic resonance of the left atrium: an open-access grand challenge.
Evaluation of current algorithms for segmentation of scar tissue from late gadolinium enhancement cardiovascular magnetic resonance of the left atrium: an open-access grand challenge.
复制标题
DOI：
10.1186/1532-429x-15-105
复制
发表时间：
2013-12-20
期刊：
影响因子：
--
通讯作者：
Rhode K
中科院分区：
文献类型：
--
作者：
Karim R;Housden RJ;Balasubramaniam M;Chen Z;Perry D;Uddin A;Al-Beyatti Y;Palkhi E;Acheampong P;Obom S;Hennemuth A;Lu Y;Bai W;Shi W;Gao Y;Peitgen HO;Radau P;Razavi R;Tannenbaum A;Rueckert D;Cates J;Schaeffter T;Peters D;MacLeod R;Rhode K
晚期钆增强 (LGE) 心血管磁共振 (CMR) 成像可用于可视化左心房 (LA) 心肌中的纤维化和疤痕区域。这对于房颤 (AF) 患者的治疗分层以及射频导管消融 (RFCA) 后的治疗评估非常重要。在本文中，我们提出了一个标准化评估基准框架，用于从 LGE CMR 图像中分割纤维化和疤痕的算法。报告的算法是对 ISBI（IEEE 国际生物医学成像研讨会）研讨会向医学成像界提出的公开挑战的回应。该图像数据库由来自 AF 患者的 60 个多中心、多供应商 LGE CMR 图像数据集组成，其中 30 个图像在 RFCA 治疗 AF 之前拍摄，30 个图像在 RFCA 治疗后拍摄。通过合并三位观察者的手动分割，建立了疤痕和纤维化的参考标准。此外，还使用 2、3 和 4 个标准差 (SD) 和半高全宽 (FWHM) 方法对疤痕进行量化。七个机构响应了这一挑战：帝国理工学院 (IC)、梅维斯·弗劳恩霍夫 (MV)、桑尼布鲁克健康科学 (SY)、哈佛大学/波士顿大学 (HB)、耶鲁大学医学院 (YL)、伦敦国王学院 (KCL) 和犹他州 CARMA (UTA、UTB)。本研究评估了 8 种不同的算法。某些算法在消融前和消融后成像方面的表现均明显优于 SD 和 FWHM 方法。消融前图像的分割具有挑战性，并且在消融后图像中发现与参考标准具有良好的相关性。与参考标准的重叠分数（满分 100）如下： Pre：IC = 37，MV = 22，SY = 17，YL = 48，KCL = 30，UTA = 42，UTB = 45；帖子：IC = 76、MV = 85、SY = 73、HB = 76、YL = 84、KCL = 78、UTA = 78、UTB = 72。研究得出的结论是，目前没有一种算法被认为明显优于其他算法。 LGE CMR 图像中 LA 纤维化和疤痕量化的算法还有进一步发展的空间。因此，未来疤痕分割算法的基准测试非常重要。拟议的基准测试框架作为开源提供，新参与者可以通过基于网络的界面评估他们的算法。
Late Gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging can be used to visualise regions of fibrosis and scarring in the left atrium (LA) myocardium. This can be important for treatment stratification of patients with atrial fibrillation (AF) and for assessment of treatment after radio frequency catheter ablation (RFCA). In this paper we present a standardised evaluation benchmarking framework for algorithms segmenting fibrosis and scar from LGE CMR images. The algorithms reported are the response to an open challenge that was put to the medical imaging community through an ISBI (IEEE International Symposium on Biomedical Imaging) workshop. The image database consisted of 60 multicenter, multivendor LGE CMR image datasets from patients with AF, with 30 images taken before and 30 after RFCA for the treatment of AF. A reference standard for scar and fibrosis was established by merging manual segmentations from three observers. Furthermore, scar was also quantified using 2, 3 and 4 standard deviations (SD) and full-width-at-half-maximum (FWHM) methods. Seven institutions responded to the challenge: Imperial College (IC), Mevis Fraunhofer (MV), Sunnybrook Health Sciences (SY), Harvard/Boston University (HB), Yale School of Medicine (YL), King’s College London (KCL) and Utah CARMA (UTA, UTB). There were 8 different algorithms evaluated in this study. Some algorithms were able to perform significantly better than SD and FWHM methods in both pre- and post-ablation imaging. Segmentation in pre-ablation images was challenging and good correlation with the reference standard was found in post-ablation images. Overlap scores (out of 100) with the reference standard were as follows: Pre: IC = 37, MV = 22, SY = 17, YL = 48, KCL = 30, UTA = 42, UTB = 45; Post: IC = 76, MV = 85, SY = 73, HB = 76, YL = 84, KCL = 78, UTA = 78, UTB = 72. The study concludes that currently no algorithm is deemed clearly better than others. There is scope for further algorithmic developments in LA fibrosis and scar quantification from LGE CMR images. Benchmarking of future scar segmentation algorithms is thus important. The proposed benchmarking framework is made available as open-source and new participants can evaluate their algorithms via a web-based interface.