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Semiautomatic three‐dimensional segmentation of the prostate using two‐dimensional ultrasound images
Author(s) -
Wang Yunqiu,
Cardinal H. Neale,
Downey Donal B.,
Fenster Aaron
Publication year - 2003
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 - 10.1118/1.1568975
Subject(s) - segmentation , boundary (topology) , prostate , image segmentation , ultrasound , artificial intelligence , computer science , computer vision , mathematics , physics , medicine , mathematical analysis , acoustics , cancer
In this paper, we report on two methods for semiautomatic three‐dimensional (3‐D) prostate boundary segmentation using 2‐D ultrasound images. For each method, a 3‐D ultrasound prostate image was sliced into the series of contiguous 2‐D images, either in a parallel manner, with a uniform slice spacing of 1 mm, or in a rotational manner, about an axis approximately through the center of the prostate, with a uniform angular spacing of 5°. The segmentation process was initiated by manually placing four points on the boundary of a selected slice, from which an initial prostate boundary was determined. This initial boundary was refined using the Discrete Dynamic Contour until it fit the actual prostate boundary. The remaining slices were then segmented by iteratively propagating this result to an adjacent slice and repeating the refinement, pausing the process when necessary to manually edit the boundary. The two methods were tested with six 3‐D prostate images. The results showed that the parallel and rotational methods had mean editing rates of 20% and 14%, and mean (mean absolute) volume errors of −5.4% (6.5%) and −1.7% (3.1%), respectively. Based on these results, as well as the relative difficulty in editing, we conclude that the rotational segmentation method is superior.