Mitchell S C, Lelieveldt B P, van der Geest R J, Bosch H G, Reiber J H, Sonka M
Department of Electrical and Computer Engineering, The University of Iowa, Iowa City 52242, USA.
IEEE Trans Med Imaging. 2001 May;20(5):415-23. doi: 10.1109/42.925294.
A fully automated approach to segmentation of the left and right cardiac ventricles from magnetic resonance (MR) images is reported. A novel multistage hybrid appearance model methodology is presented in which a hybrid active shape model/active appearance model (AAM) stage helps avoid local minima of the matching function. This yields an overall more favorable matching result. An automated initialization method is introduced making the approach fully automated. Our method was trained in a set of 102 MR images and tested in a separate set of 60 images. In all testing cases, the matching resulted in a visually plausible and accurate mapping of the model to the image data. Average signed border positioning errors did not exceed 0.3 mm in any of the three determined contours-left-ventricular (LV) epicardium, LV and right-ventricular (RV) endocardium. The area measurements derived from the three contours correlated well with the independent standard (r = 0.96, 0.96, 0.90), with slopes and intercepts of the regression lines close to one and zero, respectively. Testing the reproducibility of the method demonstrated an unbiased performance with small range of error as assessed via Bland-Altman statistic. In direct border positioning error comparison, the multistage method significantly outperformed the conventional AAM (p < 0.001). The developed method promises to facilitate fully automated quantitative analysis of LV and RV morphology and function in clinical setting.
本文报道了一种从磁共振（MR）图像中全自动分割左右心室的方法。提出了一种新颖的多阶段混合外观模型方法，其中混合主动形状模型/主动外观模型（AAM）阶段有助于避免匹配函数的局部最小值。这产生了总体上更有利的匹配结果。引入了一种自动初始化方法，使该方法完全自动化。我们的方法在一组102幅MR图像中进行训练，并在另一组60幅图像中进行测试。在所有测试案例中，匹配结果在视觉上使模型与图像数据具有合理且准确的映射。在确定的三个轮廓——左心室（LV）心外膜、LV和右心室（RV）心内膜中，平均带符号边界定位误差在任何一个轮廓中均未超过0.3毫米。从这三个轮廓得出的面积测量值与独立标准具有良好的相关性（r = 0.96、0.96、0.90），回归线的斜率和截距分别接近1和0。对该方法的可重复性进行测试表明，通过布兰德-奥特曼统计评估，该方法具有无偏差的性能且误差范围较小。在直接边界定位误差比较中，多阶段方法明显优于传统AAM（p < 0.001）。所开发的方法有望促进临床环境中LV和RV形态与功能的全自动定量分析。
