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An automatic 3D detection method of seeds on CT images

机译:CT图像上种子的3D自动检测方法

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Post-implant dosimetric evaluation is a key procedure of brachytherapy. The way of traditional manual localizing seed is lack of efficiency. Therefore, Automatic seed detection method is needed to efficiently and accurately calculate the centroid and orientation. There are some shape differences among seeds, caused by imaging features. Single seed may be presented on more than one slice and seeds locating closely may appear connected. This paper proposed an automatic three-dimensional detection method of seeds on CT images. Firstly, the areas possibly containing seeds are got by binary threshold on each CT slice and the related geometric information was recorded. Then, the larger areas containing more than one seed are segmented by watershed algorithm. According to the max seed volume rule and straight line rule, the areas are connected into the complete seed volume and the weighted centroid and orientation are calculated. The statistical analysis demonstrates that the rate of seed detection can achieve 97% and has a high applicability of post-implant dosimetric verification.
机译:植入后剂量学评估是近距离放射治疗的关键程序。传统的手工本地化种子的方式缺乏效率。因此,需要一种自动的种子检测方法来有效和准确地计算质心和方向。种子之间的形状差异是由成像特征引起的。单个种子可能会出现在多个切片上,并且位置紧密的种子可能会看起来相连。提出了一种CT图像上种子的三维自动检测方法。首先,在每个CT切片上通过二进制阈值获得可能包含种子的区域,并记录相关的几何信息。然后,通过分水岭算法对包含多个种子的较大区域进行分割。根据最大种子量规则和直线规则,将区域连接到完整种子量中,并计算加权质心和方向。统计分析表明,种子检测率可以达到97%,具有很高的植入后剂量学验证的适用性。

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