Prostate boundary segmentation from 3D ultrasound images
Prostate boundary segmentation from 3D ultrasound images
复制标题
DOI：
10.1118/1.1586267
复制
发表时间：
2003-07-01
期刊：
影响因子：
3.8
通讯作者：
Ladak, HM
中科院分区：
文献类型：
--
作者：
Hu, N;Downey, DB;Ladak, HM
分割或勾画前列腺边界是前列腺癌患者管理中的一项重要任务。本文描述了一种从三维超声图像中半自动分割前列腺的算法。该算法使用基于模型的初始化和使用高效可变形模型的网格加密。初始化只需要用户选择六个点，根据这些点使用形状信息来估计前列腺的轮廓。然后，估计的轮廓会自动变形，以更好地适应前列腺边界。编辑工具允许用户编辑有问题的区域中的边界，然后再次变形模型以改进最终结果。该算法在Pentium III 400 MHz PC上运行不到1分钟。通过将从局部和全局分析获得的算法结果与人工分割六个前列腺的结果进行比较，评估了该算法的准确性。局部差异被映射到算法边界的表面上以产生视觉表示。整体误差分析表明，人工边界与算法边界的平均误差为-0.20±-0.28 mm，平均绝对误差为1.19±0.14 mm，平均最大误差为7.01±1.04 mm，平均体积误差为7.16%+/-3.45%。还评估了人工分割和算法分割中的可变性：通过在分割网格上映射可变性来生成局部可变性的视觉表示。人工分割的平均变异为0.98 mm，算法分割的平均变异为0.63min，约51.5%的点构成的平均算法边界与人工平均边界相差不大(P<0.01)。(C)2003年美国医学物理学家协会。
Segmenting, or outlining the prostate boundary is an important task in the management of patients with prostate cancer. In this paper, an algorithm is described for semiautomatic segmentation of the prostate from 3D ultrasound images. The algorithm uses model-based initialization and mesh refinement using an efficient deformable model. Initialization requires the user to select only six points from which the outline of the prostate is estimated using shape information. The estimated outline is then automatically deformed to better fit the prostate boundary. An editing tool allows the user to edit the boundary in problematic regions and then deform the model again to improve the final results. The algorithm requires less than 1 min on a Pentium III 400 MHz PC. The accuracy of the algorithm was assessed by comparing the algorithm results, obtained from both local and global analysis, to the manual segmentations on six prostates. The local difference was mapped on the surface of the algorithm boundary to produce a visual representation. Global error analysis showed that the average difference between manual and algorithm boundaries was -0.20+/-0.28 mm, the average absolute difference was 1.19+/-0.14 mm, the average maximum difference was 7.01+/-1.04 mm, and the average volume difference was 7.16%+/-3.45%. Variability in manual and algorithm segmentation was also assessed: Visual representations of local variability were generated by mapping variability on the segmentation mesh. The mean variability in manual segmentation was 0.98 mm and in algorithm segmentation was 0.63 min and the differences of about 51.5% of the points comprising the average algorithm boundary are insignificant (Pless than or equal to0.01) to the manual average boundary. (C) 2003 American Association of Physicists in Medicine.