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Towards ultrasound cardiac image segmentation based on the radiofrequency signal.

机译:基于射频信号的超声心脏图像分割。

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In echocardiography, the radio-frequency (RF) image is a rich source of information about the investigated tissues. Nevertheless, very few works are dedicated to boundary detection based on the RF image, as opposed to envelope image. In this paper, we investigate the feasibility and limitations of boundary detection in echocardiographic images based on the RF signal. We introduce two types of RF-derived parameters: spectral autoregressive parameters and velocity-based parameters, and we propose a discontinuity adaptive framework to perform the detection task. In classical echographic cardiac acquisitions, we show that it is possible to use the spectral contents for boundary detection, and that improvement can be expected with respect to traditional methods. Using the system approach, we study on simulations how the spectral contents can be used for boundary detection. We subsequently perform boundary detection in high frame rate simulated and in vivo cardiac sequences using the variance of velocity, obtaining very promising results. Our work opens the perspective of a RF-based framework for ultrasound cardiac image segmentation and tracking.
机译:在超声心动图中,射频(RF)图像是有关所研究组织的丰富信息来源。然而,与包络图像相反,很少有工作致力于基于RF图像的边界检测。在本文中,我们研究了基于RF信号的超声心动图图像边界检测的可行性和局限性。我们介绍了两种类型的RF衍生参数:频谱自回归参数和基于速度的参数,并提出了一种不连续性自适应框架来执行检测任务。在经典的超声心动图采集中,我们表明可以将频谱内容用于边界检测,并且相对于传统方法可以期望得到改善。使用系统方法,我们在仿真中研究了如何将光谱内容用于边界检测。随后,我们使用速度的变化在高帧频模拟和体内心脏序列中执行边界检测,获得了非常有希望的结果。我们的工作为基于超声的心脏图像分割和跟踪的基于RF的框架打开了视野。

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