Automatic segmentation of the fetal cerebellum on ultrasound volumes, using a 3D statistical shape model
Automatic segmentation of the fetal cerebellum on ultrasound volumes, using a 3D statistical shape model
复制标题
DOI：
10.1007/s11517-013-1082-1
复制
发表时间：
2013-09-01
影响因子：
3.2
通讯作者：
Medina Banuelos, Veronica
中科院分区：
文献类型：
--
作者：
Gutierrez-Becker, Benjamin;Arambula Cosio, Fernando;Medina Banuelos, Veronica
先前的研究表明，对三维超声数据集的解剖结构进行分割为胎儿健康评估提供了重要工具。在这项研究中，我们提出了一种基于三维统计形状模型的算法，用于在三维超声图像中分割胎儿小脑。该模型使用一个特定的目标函数进行调整，而这个目标函数又使用Nelder - Mead单纯形算法进行优化。我们的算法在取自20名孕周在18到24周之间的孕妇的胎儿脑部超声图像上进行了测试。手动测量的小脑体积和使用我们的算法计算出的体积之间的组内相关系数为0.8528，平均Dice系数为0.8。据我们所知，这是首次在三维超声数据上对胎儿颅内结构进行自动分割的尝试。
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.