Biomedical Imaging Lab, Center of Applied Science and Technological Development, Universidad Nacional Autónoma de México, 04510 Mexico, DF, Mexico.
Med Biol Eng Comput. 2013 Sep;51(9):1021-30. doi: 10.1007/s11517-013-1082-1. Epub 2013 May 18.
Previous work has shown that the segmentation of anatomical structures on 3D ultrasound data sets provides an important tool for the assessment of the fetal health. In this work, we present an algorithm based on a 3D statistical shape model to segment the fetal cerebellum on 3D ultrasound volumes. This model is adjusted using an ad hoc objective function which is in turn optimized using the Nelder-Mead simplex algorithm. Our algorithm was tested on ultrasound volumes of the fetal brain taken from 20 pregnant women, between 18 and 24 gestational weeks. An intraclass correlation coefficient of 0.8528 and a mean Dice coefficient of 0.8 between cerebellar volumes measured using manual techniques and the volumes calculated using our algorithm were obtained. As far as we know, this is the first effort to automatically segment fetal intracranial structures on 3D ultrasound data.
先前的工作表明，对三维超声数据集上的解剖结构进行分割为评估胎儿健康提供了重要工具。在这项工作中，我们提出了一种基于三维统计形状模型的算法，用于对三维超声体数据中的胎儿小脑进行分割。该模型使用特定的目标函数进行调整，该目标函数反过来又使用 Nelder-Mead 单纯形算法进行优化。我们的算法在 20 名孕妇的胎儿大脑超声体积上进行了测试，这些孕妇的妊娠周数在 18 至 24 周之间。使用手动技术测量的小脑体积和使用我们的算法计算的小脑体积之间的组内相关系数为 0.8528，Dice 系数的平均值为 0.8。据我们所知，这是首次尝试在三维超声数据上自动分割胎儿颅内结构。
