Segmentation of the endocardial and epicardial boundaries on 3D cardiac magnetic resonance images plays a vital role in the assessment of ejection fraction, wall thickness, end-diastolic volume, end-systolic volume, and stroke volume. Accurate segmentation is significantly challenged by intensity inhomogeneity artifacts, low contrast, and ill-defined organ/region boundaries. We propose a two stage hybrid active contour model for robust left ventricle (LV) segmentation accompanied with a new initialization technique based on prior of the LV structure. The proposed approach includes a new level set method using local, spatially-varying, statistical model for image intensity, an edge-based term to capture region boundaries, and regularization functionals for smooth evolution of the segmenting curve and to avoid expensive reinitialization. Moreover, convex hull interpolation is employed to include the papillary muscles within the endocardial boundary for a refined depiction of LV geometry. The accuracy and robustness of the proposed algorithm were assessed using York, Sunnybrook and ACDC datasets (33 + 45 + 100 subjects), with a wide spectrum of normal hearts, congenital heart diseases, and cardiac dysfunction. Experiments showed that the proposed approach significantly outperformed other active contour methods (overall Dice score 0.90), generating accurate segmentations of left ventricular outflow tract (Dice score 0.91), apical slices (Dice score 0.82), systolic and diastolic phases (Dice scores 0.92 and 0.88 respectively). The percentage of good contours was about 92% and the average perpendicular distance was less than 1.8 mm. Automatically generated segmentation yielded superior estimates of ejection fraction with an R² ≥ 0.937. Furthermore, the proposed method was validated using 100 cine MRI cases consisting of five different cardiac classes from the ACDC MICCAI 2017 challenge. The proposed algorithm yielded superior segmentation performance compared with existing active contour models and other state-of-the-art cardiac segmentation techniques, with extensive validation on three standard cardiac datasets, with different cardiac pathologies and phases.

在 3D 心脏磁共振图像上分割心内膜和心外膜边界在射血分数、壁厚、舒张末期容积、收缩末期容积和每搏输出量的评估中起着至关重要的作用。强度不均匀伪影、低对比度和不明确的器官/区域边界严重挑战了准确分割。我们提出了一种用于稳健左心室 (LV) 分割的两阶段混合活动轮廓模型，并伴随着一种基于 LV 结构先验的新初始化技术。所提出的方法包括使用局部、空间变化的图像强度统计模型的新水平集方法、用于捕获区域边界的基于边缘的术语，以及用于分割曲线平滑演化并避免昂贵的重新初始化的正则化函数。而且，采用凸包插值法将心内膜边界内的乳头肌包括在内，以精细描绘 LV 几何形状。使用 York、Sunnybrook 和 ACDC 数据集（33 + 45 + 100 名受试者）评估了所提出算法的准确性和稳健性，其中包括广泛的正常心脏、先天性心脏病和心脏功能障碍。实验表明，所提出的方法明显优于其他主动轮廓方法（总体 Dice 得分 0.90），生成左心室流出道（Dice 得分 0.91）、心尖切片（Dice 得分 0.82）、收缩期和舒张期（Dice 得分 0.92 和0.88）。良好轮廓的百分比约为 92%，平均垂直距离小于 1.8 毫米。R ²  ≥ 0.937。此外，使用 100 个电影 MRI 案例验证了所提出的方法，这些案例由来自 ACDC MICCAI 2017 挑战的五种不同心脏类别组成。与现有的活动轮廓模型和其他最先进的心脏分割技术相比，所提出的算法产生了卓越的分割性能，并在三个标准心脏数据集上进行了广泛验证，具有不同的心脏病理和阶段。