Wang Yunqiu, Cardinal H Neale, Downey Donal B, Fenster Aaron
Imaging Research Laboratories, Robarts Research Institute, 100 Perth Drive, London, Ontario N6A 5K8, Canada.
Med Phys. 2003 May;30(5):887-97. doi: 10.1118/1.1568975.
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 degrees. 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.
在本文中,我们报告了两种使用二维超声图像进行半自动三维(3-D)前列腺边界分割的方法。对于每种方法,将三维超声前列腺图像切成一系列连续的二维图像,要么以平行方式,切片间距均匀为1毫米,要么以旋转方式,围绕大致穿过前列腺中心的轴,角间距均匀为5度。分割过程通过在选定切片的边界上手动放置四个点来启动,由此确定初始前列腺边界。使用离散动态轮廓对该初始边界进行细化,直到它与实际前列腺边界拟合。然后通过将此结果迭代传播到相邻切片并重复细化来分割其余切片,必要时暂停该过程以手动编辑边界。用六幅三维前列腺图像对这两种方法进行了测试。结果表明,平行法和旋转法的平均编辑率分别为20%和14%,平均(平均绝对)体积误差分别为-5.4%(6.5%)和-1.7%(3.1%)。基于这些结果以及编辑的相对难度,我们得出结论,旋转分割法更优。