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Left Ventricle wall extraction in cardiac MRI using region based level sets and vector field convolution

机译:使用基于区域的水平集和矢量场卷积在心脏MRI中左心室壁提取

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Left Ventricle imaging using short-axis MRI sequences is considered as an important tool used for evaluating cardiac function by calculating important clinical cardiac parameters. This requires manual tracing of LV wall which is subjective, tedious and time-consuming process. This paper presents semi-automatic method for left ventricle inner wall (endocardium) segmentation. This paper focuses on segmenting one complete cardiac cycle without any user intervention. The method used in this paper is region based level sets and vector field convolution active contour model out of which the later method has significantly achieved the better segmentation results. The end systolic and end diastolic volume is calculated by both the methods. The methods are tested on many images and time consumption is reduced using vector field convolution which takes only 30 iterations for segmenting one image per slice. The clinical parameters end diastolic volume, end systolic volume and ejection fraction values obtained from both methods are compared with the values of manually segmented images. The value obtained from vector field convolution gives a closer value to manual segmentation which proves the accuracy of the method and can be considered clinically significant. This semi-automatic approach provides cardiac radiologists a practical method for an accurate segmentation of left ventricle.
机译:使用短轴MRI序列的左心室成像被认为是通过计算重要的临床心脏参数来评估心功能的重要工具。这需要手动跟踪LV墙,这是主观的,乏味且耗时的过程。本文介绍了左心室内壁(内膜)分割的半自动方法。本文重点介绍在没有任何用户干预的情况下分割一个完整的心动周期。本文中使用的方法是基于区域的水平集和矢量场卷积有源轮廓模型,其中稍后的方法显着地实现了更好的分段结果。最终收缩和最终舒张体积通过这两种方法计算。这些方法在许多图像上进行测试,并且使用矢量字段卷积减少了时间消耗,这仅需要30个迭代,用于每片分割一个图像。将两种方法获得的临床参数结束舒张体积,结束收缩体积和喷射分数值与手动分段图像的值进行比较。从矢量场卷积获得的值会对手动分段提供更接近的值,这证明了方法的准确性,并且可以被视为临床显着性。这种半自动方法提供了心脏放射学家的一种实用方法,用于精确分割左心室。

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