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. 2017 Nov;12(11):1985-1993.
doi: 10.1007/s11548-017-1632-3. Epub 2017 Jun 22.

Automatic seed picking for brachytherapy postimplant validation with 3D CT images

Guobin Zhang  1   2   3 , Qiyuan Sun  1   2 , Shan Jiang  4   5 , Zhiyong Yang  3 , Xiaodong Ma  3 , Haisong Jiang  3
Affiliations

Automatic seed picking for brachytherapy postimplant validation with 3D CT images

Guobin Zhang et al. Int J Comput Assist Radiol Surg. 2017 Nov.

Abstract

Purpose: Postimplant validation is an indispensable part in the brachytherapy technique. It provides the necessary feedback to ensure the quality of operation. The ability to pick implanted seed relates directly to the accuracy of validation. To address it, an automatic approach is proposed for picking implanted brachytherapy seeds in 3D CT images.

Methods: In order to pick seed configuration (location and orientation) efficiently, the approach starts with the segmentation of seed from CT images using a thresholding filter which based on gray-level histogram. Through the process of filtering and denoising, the touching seed and single seed are classified. The true novelty of this approach is found in the application of the canny edge detection and improved concave points matching algorithm to separate touching seeds. Through the computation of image moments, the seed configuration can be determined efficiently. Finally, two different experiments are designed to verify the performance of the proposed approach: (1) physical phantom with 60 model seeds, and (2) patient data with 16 cases.

Results: Through assessment of validated results by a medical physicist, the proposed method exhibited promising results. Experiment on phantom demonstrates that the error of seed location and orientation is within ([Formula: see text]) mm and ([Formula: see text])[Formula: see text], respectively. In addition, the most seed location and orientation error is controlled within 0.8 mm and 3.5[Formula: see text] in all cases, respectively. The average process time of seed picking is 8.7 s per 100 seeds.

Conclusions: In this paper, an automatic, efficient and robust approach, performed on CT images, is proposed to determine the implanted seed location as well as orientation in a 3D workspace. Through the experiments with phantom and patient data, this approach also successfully exhibits good performance.

Keywords: Brachytherapy; CT image; Implanted seed; Location; Seed picking.

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