A novel aortic valve segmentation from ultrasound image using continuous max-flow approach
A novel aortic valve segmentation from ultrasound image using continuous max-flow approach
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DOI：
10.1109/embc.2013.6610249
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发表时间：
2013-07
期刊：
影响因子：
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通讯作者：
Yuanyuan Nie;Zhe Luo;Junfeng Cai;Lixu Gu
中科院分区：
文献类型：
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作者：
Yuanyuan Nie;Zhe Luo;Junfeng Cai;Lixu Gu
主动脉瓣的几何特征可以应用于诊断、建模和图像引导的心脏介入治疗，然而如何从超声（US）图像中准确有效地描绘主动脉瓣的方法还没有得到很好的解决。提出了一种基于概率估计和连续最大流（CMF）方法的主动脉瓣术中二维短轴超声图像分割算法。该算法首先根据图像的强度和到相应质心的距离计算5幅先验图像的复合概率估计（CPE）和单一概率估计（SPE），然后构造当前输入图像的能量函数，然后采用图形处理器（GPU）加速的CMF方法近似真实的实时地获得主动脉瓣轮廓。从3个主题获得的270幅图像的定量评价表明，该算法的结果与专家的手动分割结果（地面实况）具有良好的相关性。采用平均对称轮廓距离（ASCD）、Dice度量（DM）和可靠性（Reliability）对算法进行了评价，其评价结果分别为1.79±0.46（像素）、0.96±0.01和0.84（d=0.95），计算时间为39.23±5.02 ms/帧。
Geometric features of aortic valve can be applied in diagnostic, modeling and image-guided cardiac intervention, however methods to accurately and effectively delineate aortic valve from ultrasound (US) image are not well addressed. This paper proposes a novel aortic valve segmentation algorithm for intra-operative 2D short-axis US image using probability estimation and continuous max-flow (CMF) approach. The algorithm first calculates composite probability estimation (CPE) and single probability estimation (SPE) over 5 prior images based on both intensity and distance to the corresponding centroid, then the energy function for the current input image is constructed, followed by a Graphic Processing Unit (GPU) accelerated CMF approach to achieve aortic valve contours in approximately real time. Quantitative evaluations over 270 images acquired from 3 subjects indicated the results of the algorithm had good correlation with the manual segmentation results (ground truth) by an expert. The Average Symmetric Contour Distance (ASCD), Dice Metric (DM), and Reliability were employed to evaluate our algorithm, and the evaluation results of these three metrics were 1.79±0.46 (in pixels), 0.96±0.01 and 0.84 (d=0.95) respectively, where the computational time was 39.23±5.02 ms per frame.