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A new method for robust organ positioning in CT images

机译:一种在CT图像中稳健器官定位的新方法

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Robust initialization is essential for any successful segmentation process of medical images. For CT images, initialization is challenging because quality, appearance, content, and field-of-view of the images are highly variable. Furthermore, high execution speed is desirable, whereas the user tolerance to errors is low in clinical applications. We present a new method for efficient and robust positioning of organs in CT images. It is based on a novel probabilistic atlas that, given a tissue type, describes the probability density of the random variable spatial location. Random sampling is then employed to select the most informative points for matching. We present results on pelvic and abdominal images acquired for radiotherapy planning.
机译:强大的初始化对于任何成功的医学图像分割过程都是至关重要的。对于CT图像,初始化很有挑战性,因为图像的质量,外观,内容和视场变化很大。此外,期望高执行速度,而在临床应用中用户对错误的容忍度较低。我们提出了一种有效和鲁棒的器官定位在CT图像中的新方法。它基于一种新颖的概率图集,给定一种组织类型,该图集描述了随机可变空间位置的概率密度。然后采用随机抽样来选择最有用的点进行匹配。我们在放射治疗计划中获得的骨盆和腹部图像上呈现结果。

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