Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia.
University Malaya Research Imaging Centre, University of Malaya, Kuala Lumpur, Malaysia.
J Magn Reson Imaging. 2018 Jul;48(1):140-152. doi: 10.1002/jmri.25932. Epub 2018 Jan 9.
Left ventricle (LV) structure and functions are the primary assessment performed in most clinical cardiac MRI protocols. Fully automated LV segmentation might improve the efficiency and reproducibility of clinical assessment.
To develop and validate a fully automated neural network regression-based algorithm for segmentation of the LV in cardiac MRI, with full coverage from apex to base across all cardiac phases, utilizing both short axis (SA) and long axis (LA) scans.
Cross-sectional survey; diagnostic accuracy.
In all, 200 subjects with coronary artery diseases and regional wall motion abnormalities from the public 2011 Left Ventricle Segmentation Challenge (LVSC) database; 1140 subjects with a mix of normal and abnormal cardiac functions from the public Kaggle Second Annual Data Science Bowl database.
FIELD STRENGTH/SEQUENCE: 1.5T, steady-state free precession.
Reference standard data generated by experienced cardiac radiologists. Quantitative measurement and comparison via Jaccard and Dice index, modified Hausdorff distance (MHD), and blood volume.
Paired t-tests compared to previous work.
Tested against the LVSC database, we obtained 0.77 ± 0.11 (Jaccard index) and 1.33 ± 0.71 mm (MHD), both metrics demonstrating statistically significant improvement (P < 0.001) compared to previous work. Tested against the Kaggle database, the signed difference in evaluated blood volume was +7.2 ± 13.0 mL and -19.8 ± 18.8 mL for the end-systolic (ES) and end-diastolic (ED) phases, respectively, with a statistically significant improvement (P < 0.001) for the ED phase.
A fully automated LV segmentation algorithm was developed and validated against a diverse set of cardiac cine MRI data sourced from multiple imaging centers and scanner types. The strong performance overall is suggestive of practical clinical utility.
3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.
左心室（LV）的结构和功能是大多数临床心脏 MRI 方案中的主要评估内容。完全自动化的 LV 分割可能会提高临床评估的效率和可重复性。
开发和验证一种基于神经网络回归的完全自动化 LV 分割算法，用于心脏 MRI 的 LV 分割，涵盖所有心脏相位的心底到心尖，同时利用短轴（SA）和长轴（LA）扫描。
横截面调查；诊断准确性。
共有 200 名患有冠状动脉疾病和局部壁运动异常的受试者，来自公开的 2011 年左心室分割挑战赛（LVSC）数据库；1140 名患有正常和异常心脏功能的受试者，来自公开的 Kaggle 第二届年度数据科学碗数据库。
磁场强度/序列：1.5T，稳态自由进动。
由有经验的心脏放射科医生生成参考标准数据。通过 Jaccard 和 Dice 指数、改进的 Hausdorff 距离（MHD）和血容量进行定量测量和比较。
与之前的工作进行配对 t 检验。
与 LVSC 数据库进行测试，我们获得了 0.77±0.11（Jaccard 指数）和 1.33±0.71mm（MHD），这两个指标都显示出与之前的工作相比有统计学意义的改善（P<0.001）。与 Kaggle 数据库进行测试，在收缩末期（ES）和舒张末期（ED）阶段，评估的血容量的符号差异分别为+7.2±13.0mL 和-19.8±18.8mL，ED 阶段的差异具有统计学意义（P<0.001）。
开发了一种完全自动化的 LV 分割算法，并针对来自多个成像中心和扫描仪类型的多种心脏电影 MRI 数据进行了验证。整体性能较强，提示具有实际的临床应用价值。
3 技术功效：第 2 阶段 J. 磁共振成像 2018 年。
