首页> 外文期刊>Investigative radiology >Multiview active appearance models for simultaneous segmentation of cardiac 2- and 4-chamber long-axis magnetic resonance images.
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Multiview active appearance models for simultaneous segmentation of cardiac 2- and 4-chamber long-axis magnetic resonance images.

机译:用于同时分割心脏2腔和4腔长轴磁共振图像的Multiview活动外观模型。

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RATIONALE AND OBJECTIVES: Long-axis cardiac magnetic resonance (MR) views enable a rapid, online evaluation of cardiac function from only 2 views. In this article, we aimed to evaluate a model-based method for the simultaneous detection of 2- and 4-chamber endocardial and epicardial contours in end-diastolic and end-systolic phases of MR images. METHODS: We introduce multiview Active Appearance Models for the automated segmentation of long-axis cardiac MR images of the left ventricle. Two modes of initialization were used to test the accuracy of the model with minimal user interaction and the best-obtainable accuracy with this model. The segmentation was initialized by annotating 2 points in the base and one in the apex. We tested the method's performance by comparing the point-to-curve errors, ejection fractions, and biplane area-length volumes calculated with the automatically detected contours to those calculated from contours that were annotated manually by experts. Leave-one-out experiments were performed with 2- and 4-chamber long axis MR images of 59 subjects in end-diastolic and end-systolic phases. RESULTS: When initializing in all 4 frames, 97% of the segmentations were successful, and the standard deviation in the volume-errors with respect to the average manually identified volume was 9.0% for the end-diastolic volumes and 15% for the end-systolic volumes. When the method was initialized in the end-systolic frames only, 92% of the segmentations were successful, and the standard deviation in the errors in the volumes with respect to the average manually identified volume was 13.3% for the end-diastolic volumes and 16.7% for the end-systolic volumes. Bland-Altman plots showed that the errors were distributed randomly around 0, and by using a paired t test comparing manual and computer-determined volumes, we were able to detect that the volume differences were not significant. Simultaneous detection of the endocardial and epicardial contours in 2- and 4-chamber views and end-diastolic and end-systolic phases for one subject takes approximately 3 seconds. CONCLUSIONS: The accuracy of the reported method is comparable with the interobserver variability for initialization in all frames and slightly worse than the interobserver variability with initialization in the end-systolic frames only. However, in both cases the errors were not significant. Initialization in end-systolic frames only leads to a statistically insignificantly lower model accuracy; however, it requires only half the user interaction. Therefore, we can conclude that this method enables rapid analysis of the cardiac left ventricular function with little user interaction.
机译:理由和目标:长轴心脏磁共振(MR)视图仅从2个视图就可以快速,在线评估心脏功能。在本文中,我们旨在评估一种基于模型的方法,用于同时检测MR图像的舒张末期和收缩末期的2腔和4腔心内膜和心外膜轮廓。方法:我们引入多视图主动外观模型,用于自动分割左心室的长轴心脏MR图像。两种初始化模式用于通过最少的用户交互和使用此模型获得的最佳准确性来测试模型的准确性。通过注释基础中的2个点和顶点中的一个点来初始化分割。我们通过比较用自动检测到的轮廓计算出的点到曲线的误差,喷射分数和双平面面积长度体积与专家手工注释的轮廓所计算出的曲线之间的误差,喷射分数和双平面面积长度体积,来测试该方法的性能。对舒张末期和收缩末期的59位受试者的2室和4室长轴MR图像进行了留一法实验。结果:在所有4帧中初始化时,成功进行了97%的分割,相对于人工识别的平均体积,容积误差的标准偏差为舒张末期容积为9.0%,舒张末期容积为15%。收缩期容积。当仅在收缩末期帧中初始化该方法时,成功进行了92%的分割,并且舒张末期体积相对于手动识别的平均体积的体积误差的标准偏差为13.3%,舒张末期体积为16.7 %代表收缩末期容积。布兰德-奥特曼(Bland-Altman)图显示,误差在0附近随机分布,通过使用配对t检验比较手动和计算机确定的体积,我们能够检测到体积差异不显着。同时检测一名受试者的2腔和4腔内膜的心内膜和心外膜轮廓以及舒张末期和收缩末期。结论:所报道方法的准确性与观察者间变异性在所有帧中的初始化是可比的,并且比观察者间变异性仅在收缩期末帧中的初始化稍差。但是,在这两种情况下,错误都不明显。在收缩末期帧中初始化只会导致统计学上较低的模型准确性;但是,它只需要用户交互的一半。因此,我们可以得出结论,这种方法可以在几乎没有用户交互的情况下对心脏左心室功能进行快速分析。

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