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首页> 外文会议>2010 3rd International Conference on Biomedical Engineering and Informatics >The Monte Carlo method for the evaluation of automatic recontouring algorithms accuracy
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The Monte Carlo method for the evaluation of automatic recontouring algorithms accuracy

机译：蒙特卡洛方法用于自动轮廓重构算法准确性评估

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Characterizing the performance of automated contouring algorithm has been a persistent challenge. The accuracy of automated contouring algorithm of medical images has been difficult to quantify because of the absence of a ground truth segmentation of clinical data. A common strategy is to compare the automated contour with the segmentation produced by an expert or by a group of experts. The known problem is that inter-observer and intra-observer variability is very high especially when images to be segmented have low contrast. We present a Monte Carlo method that accounts for this variability and uses it to generate a set of random contours. Then we compare automatic contour with each sampled contours obtaining an estimate of the probability that automatic method is accurate. Our method seems to be able to provide more information than the classical evaluation of similarity between automatic and expert contours does, therefore avoiding the problem of constructing a ground truth.
机译：表征自动轮廓算法的性能一直是一个持续的挑战。由于没有临床数据的地面真实分割，因此难以量化医学图像自动轮廓算法的准确性。常见的策略是将自动轮廓与专家或一组专家进行的分割进行比较。已知的问题是观察者之间和观察者内部的可变性非常高，尤其是在要分割的图像对比度较低时。我们提出了一种解决此可变性的蒙特卡洛方法，并使用它来生成一组随机轮廓。然后，我们将自动轮廓与每个采样轮廓进行比较，以获得自动方法准确的概率估计。我们的方法似乎能够提供比自动轮廓线和专家轮廓线之间的经典相似度评估更多的信息，因此避免了构造基本事实的问题。

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