Cardiac image segmentation using generalized polynomial chaos expansion and level set function

Cardiac image segmentation using generalized polynomial chaos expansion and level set function
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使用广义多项式混沌展开和水平集函数进行心脏图像分割

DOI:
10.1109/embc.2017.8036909
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发表时间:
2017
期刊:
2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC
影响因子:
--
通讯作者:
Du, Dongping
Du, Dongping
中科院分区:
--
文献类型:
--
作者:
Du, Yuncheng;Du, Dongping

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心血管磁共振(CMR)图像涉及大量的不确定性。这种不确定性可能源于固有的测量限制或患者之间的异质性。如果不适当考虑这些不确定性,图像分析可能会提供不准确的心脏功能估计,并最终导致错误的诊断和不适当的治疗策略。在这项工作中,一个随机的图像分割算法的开发,以分离心脏腔室的背景CMR图像。考虑到噪声和像素值的不确定性,将广义多项式混沌(gPC)扩展与水平集函数集成,以动态地演化心腔的边界。开发了两个连续的步骤:确定性分割以识别边界的直接邻域,其中像素值用于校准gPC模型;以及随机分割,其应用于邻域区域以随机方式演化心腔的边界。该方法可以提供分割的心脏边界的概率描述,这将大大提高图像分析的可靠性,并潜在地增强心脏功能评估。
Cardiovascular Magnetic Resonance (CMR) images involves a great amount of uncertainties. Such uncertainties may originate from either intrinsic measurement limitations or heterogeneities among patients. Without properly considering these uncertainties, image analysis may provide inaccurate estimations of cardiac functions, and ultimately lead to false diagnosis and inappropriate treatment strategy. In this work, a stochastic image segmentation algorithm is developed to separate cardiac chambers from the background of CMR images. To account for noise and uncertainties in pixel values, a generalized polynomial chaos (gPC) expansion is integrated with a level set function to dynamically evolve boundaries of cardiac chambers. Two consecutive steps are developed: a deterministic segmentation to identify an immediate neighborhood of boundary, of which pixel values are used to calibrate the gPC model; and a stochastic segmentation applied to the neighborhood region to evolve boundaries of cardiac chambers in a stochastic manner. The proposed method can provide a probabilistic description of the segmented heart boundary, which will greatly improve the reliability of image analysis, and potentially enhanced cardiac function evaluation.
使用不确定速度的水平集传播对随机图像进行分割
DOI: --
发表时间: 2013
影响因子: 2
作者:
T. Pätz;T. Preußer
通讯作者: T. Preußer
DOI: --
发表时间: 2017
期刊:
影响因子: --
作者:
Yuncheng Du;H. Budman;T. Duever
通讯作者: T. Duever