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Needle segmentation using 3D Hough transform in 3D TRUS guided prostate transperineal therapy
Author(s) -
Qiu Wu,
Yuchi Ming,
Ding Mingyue,
Tessier David,
Fenster Aaron
Publication year - 2013
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4795337
Subject(s) - hough transform , brachytherapy , prostate , prostate brachytherapy , segmentation , 3d ultrasound , medicine , contouring , artificial intelligence , computer vision , ultrasound , computer science , radiology , radiation therapy , cancer , computer graphics (images) , image (mathematics)
Purpose: Prostate adenocarcinoma is the most common noncutaneous malignancy in American men with over 200 000 new cases diagnosed each year. Prostate interventional therapy, such as cryotherapy and brachytherapy, is an effective treatment for prostate cancer. Its success relies on the correct needle implant position. This paper proposes a robust and efficient needle segmentation method, which acts as an aid to localize the needle in three‐dimensional (3D) transrectal ultrasound (TRUS) guided prostate therapy.Methods: The procedure of locating the needle in a 3D TRUS image is a three‐step process. First, the original 3D ultrasound image containing a needle is cropped; the cropped image is then converted to a binary format based on its histogram. Second, a 3D Hough transform based needle segmentation method is applied to the 3D binary image in order to locate the needle axis. The position of the needle endpoint is finally determined by an optimal threshold based analysis of the intensity probability distribution. The overall efficiency is improved through implementing a coarse‐fine searching strategy. The proposed method was validated in tissue‐mimicking agar phantoms, chicken breast phantoms, and 3D TRUS patient images from prostate brachytherapy and cryotherapy procedures by comparison to the manual segmentation. The robustness of the proposed approach was tested by means of varying parameters such as needle insertion angle, needle insertion length, binarization threshold level, and cropping size.Results: The validation results indicate that the proposed Hough transform based method is accurate and robust, with an achieved endpoint localization accuracy of 0.5 mm for agar phantom images, 0.7 mm for chicken breast phantom images, and 1 mm for in vivo patient cryotherapy and brachytherapy images. The mean execution time of needle segmentation algorithm was 2 s for a 3D TRUS image with size of 264 × 376 × 630 voxels.Conclusions: The proposed needle segmentation algorithm is accurate, robust, and suitable for 3D TRUS guided prostate transperineal therapy.