Real-time aortic valve segmentation from transesophageal echocardiography sequence
Real-time aortic valve segmentation from transesophageal echocardiography sequence
复制标题
根据经食管超声心动图序列进行实时主动脉瓣分割
DOI：
10.1007/s11548-014-1104-y
复制
发表时间：
2015-04
影响因子：
3
通讯作者：
Gu, Lixu
中科院分区：
文献类型：
--
作者：
Zhuang, Xiahai;Nie, Yuanyuan;Luo, Zhe;Gu, Lixu
目的主动脉瓣的几何特征在临床诊断、形状建模和图像引导心脏介入等许多应用中发挥着重要作用，特别是在经导管主动脉瓣植入术（TAVI）中。然而，很少有关于经食管超声心动图（TEE）序列主动脉瓣分割的研究报道。为了获得准确的分割结果，进一步为TAVI提供有效的支持，本文提出了一种利用改进的概率估计和连续最大流量（CMF）方法从术中短轴视图TEE序列中实时分割主动脉瓣的方法。方法提出的主动脉瓣分割方法包括两个关键阶段：(1)在概率估计阶段，首先选择5个不同的先验帧，并由专家对主动脉瓣进行人工分割。然后，基于径向平均强度和径向距离分别构建了5帧先验帧的改进复合概率估计（CPE）和单概率估计（SPE）；(2)在能量函数构建阶段，计算相似度度量，找出当前输入TEE帧与先验帧之间的匹配指数。因此，使用先验图像的典型前景和背景强度来构建相应的能量函数。最后，采用图形处理单元（GPU）加速的CMF方法实时获得主动脉瓣轮廓。结果评价研究共包含30个序列，每个序列包含62-146个短轴TEE帧。将结果与人工分割（ground truth）进行比较。算法的平均对称轮廓距离（ASCD）、骰子度量（DM）和可靠性分别达到0.850.21 mm、0.960.01和0.90()，每帧计算时间为57.048.98 ms。结论实验结果表明，该方法可以实现短轴视图TEE序列对主动脉瓣的准确实时分割。
PurposeGeometric features of the aortic valve play an important role in many applications, such as the clinical diagnostics, shape modeling and image-guided cardiac interventions, especially for the transcatheter aortic valve implantation (TAVI) procedure. However, few works have been reported on the topic of aortic valve segmentation from transesophageal echocardiography (TEE) sequences. To obtain accurate segmentation results and further provide valid support for TAVI, this paper presents a real-time method for segmenting the aortic valve from intraoperative, short-axis view TEE sequences, using an improved probability estimation and continuous max-flow (CMF) approach.MethodsThe proposed segmentation method includes two key stages: (1) In the probability estimation stage, five different prior frames spanning a cardiac circle are firstly selected with the aortic valve manually segmented by an expert. Then, the improved composite probability estimation (CPE) and single probability estimation (SPE) over the five prior frames are, respectively, constructed based on their radial average intensity and radial distance. (2) In the energy function construction stage, the similarity metric is calculated to find out the matching exponents between the current input TEE frame and the prior frames. The typical foreground and background intensities of prior images are therefore used to construct the corresponding energy function. Finally, the CMF approach, accelerated with a graphic processing unit (GPU), is employed to achieve the aortic valve contours in real time.ResultsThe evaluation study contained 30 sequences, with each containing 62–146 short-axis TEE frames. The results were compared with the manual segmentation (ground truth). The average symmetric contour distance (ASCD), dice metric (DM) and the reliability of the algorithm reached 0.850.21 mm, 0.960.01 and 0.90 (), respectively, and the computation time was 57.048.98 ms per frame.ConclusionThe experiment results reveal that the proposed method can achieve accurate and real-time segmentation of aortic valve from TEE sequence of short-axis view.
登录
查看更多内容
影响因子：
10.6
作者：
Ivo Wolf;M. Hastenteufel;Raffaele De Simone;M. Vetter;G. Glombitza;S. Mottl-Link;Christian F Vahl;H. Meinzer
通讯作者：
Ivo Wolf;M. Hastenteufel;Raffaele De Simone;M. Vetter;G. Glombitza;S. Mottl-Link;Christian F Vahl;H. Meinzer
DOI：
10.1109/cvpr.2009.5206636
发表时间：
2009-06
期刊：
2009 IEEE Conference on Computer Vision and Pattern Recognition
影响因子：
--
作者：
D. Damen;David C. Hogg
通讯作者：
D. Damen;David C. Hogg
影响因子：
10.6
作者：
Schneider RJ;Perrin DP;Vasilyev NV;Marx GR;del Nido PJ;Howe RD
通讯作者：
Howe RD
DOI：
10.1109/embc.2013.6610249
发表时间：
2013-07
期刊：
2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
影响因子：
--
作者：
Yuanyuan Nie;Zhe Luo;Junfeng Cai;Lixu Gu
通讯作者：
Yuanyuan Nie;Zhe Luo;Junfeng Cai;Lixu Gu
DOI：
10.1109/cvpr.2010.5539903
发表时间：
2010-06
期刊：
2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
影响因子：
--
作者：
Jing Yuan;Egil Bae;X. Tai
通讯作者：
Jing Yuan;Egil Bae;X. Tai