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首页> 外文期刊>Medical image analysis >A level set framework with a shape and motion prior for segmentation and region tracking in echocardiography.
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A level set framework with a shape and motion prior for segmentation and region tracking in echocardiography.

机译:一个水平集框架,其形状和运动先于超声心动图中的分割和区域跟踪。

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摘要

We describe a level set formulation using both shape and motion prior, for both segmentation and region tracking in high frame rate echocardiographic image sequences. The proposed approach uses the following steps: registration of the prior shape, level set segmentation constrained through the registered shape and region tracking. Registration of the prior shape is expressed as a rigid or an affine transform problem, where the transform minimizing a global region-based criterion is sought. This criterion is based on image statistics and on the available estimated axial motion data. The segmentation step is then formulated through front propagation, constrained with the registered shape prior. The same region-based criterion is used both for the registration and the segmentation step. Region tracking is based on the motion field estimated from the interframe level set evolution. The proposed approach is applied to high frame rate echocardiographic sequences acquired in vivo. In this particular application, the prior shape is provided by a medical expert and the rigid transform is used for registration. It is shown that this approach provides consistent results in terms of segmentation and stability through the cardiac cycle. In particular, a comparison indicates that the results provided by our approach are very close to the results obtained with manual tracking performed by an expert cardiologist on a Doppler Tissue Imaging (DTI) study. These preliminary results show the ability of the method to perform region tracking and its potential for dynamic parametric imaging of the heart.
机译:我们描述了同时使用形状和运动的水平集公式,用于高帧速超声心动图图像序列中的分割和区域跟踪。所提出的方法使用以下步骤:先验形状的配准,通过已配准的形状和区域跟踪约束的水平集分割。先验形状的配准表示为刚性或仿射变换问题,其中寻求使基于全局区域的准则最小化的变换。该标准基于图像统计数据和可用的估计轴向运动数据。然后,通过前面的传播,并先于已注册的形状进行约束,制定分割步骤。相同的基于区域的标准用于注册和分割步骤。区域跟踪基于从帧间级别集演变估计的运动场。拟议的方法应用于体内获得的高帧速超声心动图序列。在该特定应用中,由医学专家提供先前的形状,并且将刚性变换用于配准。结果表明,这种方法在整个心动周期的分割和稳定性方面提供了一致的结果。尤其是,比较表明,我们的方法提供的结果与多普勒组织成像(DTI)研究中由专业心脏病专家进行的手动跟踪所获得的结果非常接近。这些初步结果显示了该方法执行区域跟踪的能力及其在心脏动态参数成像中的潜力。

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