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An Optimization Based Approach Embedded in a Fuzzy Connectivity Algorithm for Airway Tree segmentation

机译:嵌入基于优化的气道树分段模糊连通算法方法

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The main problem with airway segmentation methods which significantly influences their accuracy is leakage into the extra-luminal regions due to thinness of the airway wall during the process of segmentation. This phenomenon potentially makes large regions of lung-parenchyma to be wrongly identified as airways. A solution to this problem in the previous methods was based on leak detection followed by reducing leakage during the segmentation process. This has been dealt with adjusting the segmentation parameters and performing the re-segmentation process on the pre-segmented area. This makes the algorithm very exhaustive and more dependent on the user interaction. The method presented here is to apply a mathematical shape optimization approach embedded in an efficient fuzzy connectivity based segmentation algorithm to preserve shape features of the object to perform a precise segmentation. The novelty of our proposed scheme is to prevent the leakage root rather than taking leak detection and reduction approaches. This is carried out by minimizing a gradient based cost function which employs shape features of circle/cylindrical structure of airway tree during the process of segmentation.
机译:气道分割方法的主要问题在分割过程中由于气道壁的薄度而显着影响其精度的泄漏进入超腔区域。这种现象潜在地使大量的肺部牙龈地区被错误地确定为气道。在先前的方法中对该问题的解决方案是基于泄漏检测,然后在分割过程中减少泄漏。已经处理了调整分段参数并在预分段区域执行重新分割过程。这使得算法非常详尽的且更依赖于用户交互。这里呈现的方法是应用一种数学形状优化方法,其嵌入在高效的模糊连接基于基于的分割算法中,以保持对象的形状特征来执行精确的分割。我们提出的计划的新颖性是防止泄漏根,而不是筛选泄漏检测和减少方法。这是通过最小化基于梯度的成本函数来执行,该成本函数在分割过程中采用气道树的圆/圆柱结构的形状特征。

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