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An automatic 3D detection method of seeds on CT images

机译:CT图像种子的自动3D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:后植入后剂量测量是近距离放射治疗的关键程序。传统手动定位种子的方式缺乏效率。因此,需要自动种子检测方法,以有效准确地计算质心和方向。成像特征引起的种子之间存在一些形状差异。单种子可以在多于一个切片上呈现,并且可以看出定位的种子可能会出现。本文提出了一种在CT图像上的种子的自动三维检测方法。首先,通过在每个CT片上的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,通过流域算法对含有多于一个种子的较大区域进行分割。根据最大种子体积规则和直线规则,区域连接到完整的种子体积中,并计算加权质心和取向。统计分析表明,种子检测率可以达到97%,具有高于植入后剂量验证的高适用性。

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