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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中提取左心室壁

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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。这需要手动追踪左心室壁,这是主观,乏味且耗时的过程。本文提出了一种半自动的左心室内壁(心内膜)分割方法。本文的重点是在没有任何用户干预的情况下分割一个完整的心动周期。本文使用的方法是基于区域的水平集和矢量场卷积主动轮廓模型,其中后一种方法显着取得了较好的分割效果。两种方法均计算出收缩末期和舒张末期的体积。该方法在许多图像上进行了测试,并使用矢量场卷积减少了时间消耗,该向量场卷积仅需要进行30次迭代即可对每个切片进行分割。将两种方法获得的临床参数舒张末期容积,收缩末期容积和射血分数值与手动分割图像的值进行比较。从矢量场卷积获得的值与手动分割值相近,这证明了该方法的准确性,可以认为具有临床意义。这种半自动方法为心脏放射科医生提供了一种准确分割左心室的实用方法。

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