Oghli Mostafa Ghelich, Dehlaghi Vahab, Zadeh Ali Mohammad, Fallahi Alireza, Pooyan Mohammad
Department of Biomedical Engineering, Kermanshah University of Medical Sciences, Kermanshah, Iran.
Department of Radiology, Shaheed Rajaei Cardiovascular, Medical and Research Center, Tehran, Iran.
J Med Signals Sens. 2014 Jul;4(3):211-22.
Assessment of cardiac right-ventricle functions plays an essential role in diagnosis of arrhythmogenic right ventricular dysplasia (ARVD). Among clinical tests, cardiac magnetic resonance imaging (MRI) is now becoming the most valid imaging technique to diagnose ARVD. Fatty infiltration of the right ventricular free wall can be visible on cardiac MRI. Finding right-ventricle functional parameters from cardiac MRI images contains segmentation of right-ventricle in each slice of end diastole and end systole phases of cardiac cycle and calculation of end diastolic and end systolic volume and furthermore other functional parameters. The main problem of this task is the segmentation part. We used a robust method based on deformable model that uses shape information for segmentation of right-ventricle in short axis MRI images. After segmentation of right-ventricle from base to apex in end diastole and end systole phases of cardiac cycle, volume of right-ventricle in these phases calculated and then, ejection fraction calculated. We performed a quantitative evaluation of clinical cardiac parameters derived from the automatic segmentation by comparison against a manual delineation of the ventricles. The manually and automatically determined quantitative clinical parameters were statistically compared by means of linear regression. This fits a line to the data such that the root-mean-square error (RMSE) of the residuals is minimized. The results show low RMSE for Right Ventricle Ejection Fraction and Volume (≤ 0.06 for RV EF, and ≤ 10 mL for RV volume). Evaluation of segmentation results is also done by means of four statistical measures including sensitivity, specificity, similarity index and Jaccard index. The average value of similarity index is 86.87%. The Jaccard index mean value is 83.85% which shows a good accuracy of segmentation. The average of sensitivity is 93.9% and mean value of the specificity is 89.45%. These results show the reliability of proposed method in these cases that manual segmentation is inapplicable. Huge shape variety of right-ventricle led us to use a shape prior based method and this work can develop by four-dimensional processing for determining the first ventricular slices.
评估心脏右心室功能在致心律失常性右心室发育不良（ARVD）的诊断中起着至关重要的作用。在临床检查中，心脏磁共振成像（MRI）正成为诊断ARVD最有效的成像技术。右心室游离壁的脂肪浸润在心脏MRI上可见。从心脏MRI图像中获取右心室功能参数包括在心动周期的舒张末期和收缩末期各切片中对右心室进行分割，以及计算舒张末期和收缩末期容积以及其他功能参数。这项任务的主要问题在于分割部分。我们使用了一种基于可变形模型的稳健方法，该方法利用形状信息对短轴MRI图像中的右心室进行分割。在心动周期的舒张末期和收缩末期从心底到心尖对右心室进行分割后，计算这些阶段右心室的容积，然后计算射血分数。我们通过与心室的手动勾勒进行比较，对自动分割得出的临床心脏参数进行了定量评估。通过线性回归对手动和自动确定的定量临床参数进行了统计学比较。这使一条线拟合数据，以使残差的均方根误差（RMSE）最小化。结果显示右心室射血分数和容积的RMSE较低（右心室射血分数≤0.06，右心室容积≤10 mL）。还通过包括敏感性、特异性、相似性指数和杰卡德指数在内的四种统计量对分割结果进行评估。相似性指数的平均值为86.87%。杰卡德指数平均值为83.85%，这表明分割具有良好的准确性。敏感性平均值为93.9%，特异性平均值为89.45%。这些结果表明在手动分割不适用的情况下所提出方法的可靠性。右心室巨大的形状差异导致我们使用基于形状先验的方法，并且这项工作可以通过四维处理来确定最初的心室切片得到进一步发展。
