BioImaging Laboratory, Department of Bioengineering, University of Louisville, Louisville, Kentucky 40292, USA.
Med Phys. 2013 Sep;40(9):092302. doi: 10.1118/1.4817478.
The authors propose 3D (2D + time) novel, fast, robust, bidirectional coupled parametric deformable models that are capable of segmenting left ventricle (LV) wall borders using first- and second-order visual appearance features. The authors examine the effect of the proposed segmentation method on the estimation of global cardiac performance indexes.
First-order visual appearance of the cine cardiac magnetic resonance (CMR) signals (inside and outside the boundary of the deformable model) is modeled with an adaptive linear combination of discrete Gaussians (LCDG). Second-order visual appearance of the LV wall is accurately modeled with a translational and rotation-invariant second-order Markov-Gibbs random field (MGRF). The LCDG parameters are estimated using our previously proposed modification of the EM algorithm, and the potentials of rotationally invariant MGRF are computed analytically.
The authors tested the proposed segmentation approach on 15 cine CMR data sets using the Dice similarity coefficient (DSC) and the average distance (AD) between the ground truth and automated segmentation contours. The authors documented an average DSC value of 0.926 ± 0.022 and an average AD value of 2.16 ± 0.60 mm compared to two other level set methods that achieve an average DSC values of 0.904 ± 0.033 and 0.885 ± 0.02; and an average AD values of 2.86 ± 1.35 mm and 5.72 ± 4.70 mm, respectively.
The proposed segmentation approach demonstrated superior performance over other methods. Specifically, the comparative results on the publicly available MICCAI 2009 Cardiac MR Left Ventricle Segmentation database documented superior performance of the proposed approach over published methods. Additionally, the high accuracy of our segmentation approach leads to accurate estimation of the global performance indexes, as evidenced by the Bland-Altman analyses of the end-systolic volume (ESV), end-diastolic volume (EDV), and the ejection fraction (EF) ratio.
作者提出了 3D（2D+时间）新型、快速、鲁棒、双向耦合参数变形模型，能够使用一阶和二阶视觉外观特征对左心室（LV）壁边界进行分割。作者研究了所提出的分割方法对全局心脏性能指标估计的影响。
使用自适应离散高斯（LCDG）线性组合对电影心脏磁共振（CMR）信号的一阶视觉外观（变形模型内外边界）进行建模。使用平移和旋转不变二阶马尔可夫-吉布斯随机场（MGRF）对 LV 壁的二阶视觉外观进行精确建模。使用我们之前提出的 EM 算法的修改版来估计 LCDG 参数，并分析计算旋转不变 MGRF 的势。
作者在 15 个电影 CMR 数据集上使用 Dice 相似系数（DSC）和真实与自动分割轮廓之间的平均距离（AD）对所提出的分割方法进行了测试。与其他两种水平集方法相比，作者记录的平均 DSC 值为 0.926±0.022，平均 AD 值为 2.16±0.60mm，而其他两种水平集方法的平均 DSC 值为 0.904±0.033 和 0.885±0.02，平均 AD 值为 2.86±1.35mm 和 5.72±4.70mm。
所提出的分割方法的性能优于其他方法。具体来说，在公开的 MICCAI 2009 心脏磁共振左心室分割数据库上的比较结果表明，与已发表的方法相比，所提出的方法具有优越的性能。此外，我们的分割方法具有高精度，这可以通过对收缩末期容积（ESV）、舒张末期容积（EDV）和射血分数（EF）比的 Bland-Altman 分析得到证明。
