Construction of a Spatiotemporal Statistical Shape Model of Pediatric Liver from Cross-Sectional Data
Construction of a Spatiotemporal Statistical Shape Model of Pediatric Liver from Cross-Sectional Data
复制标题
根据横截面数据构建儿童肝脏时空统计形状模型
DOI：
10.1007/978-3-030-00934-2_75
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
A. Shimizu
中科院分区：
文献类型：
--
作者：
Atsushi Saito;Koyo Nakayama;A. R. Porras;Awais Mansoor;E. Biggs;M. Linguraru;A. Shimizu
本文提出了一种儿童肝脏的时空统计形状模型，该模型在腹部的计算机辅助诊断中具有潜在的应用价值。在水平集函数的空间中分析形状，这比传统研究中常用的微分同构框架具有计算优势。我们首先使用带自适应带宽的核回归技术计算平均形状发展的时变平均值。然后，使用主成分分析计算每个时间点的特征模态，并增加正则化项以确保特征模态随时间变化的平滑性。为了进一步提高性能，我们使用基于水平集的非线性变形技术进行数据增强。该算法在一个肝脏的时空统计形状建模的背景下进行了评估，该模型使用了42个人工分割的肝脏，这些肝脏来自年龄从大约2周到95个月不等的儿童。与传统方法相比，该方法具有更高的泛化能力和特异性。
This paper proposes a spatiotemporal statistical shape model of a pediatric liver, which has potential applications in computer-aided diagnosis of the abdomen. Shapes are analyzed in the space of a level set function, which has computational advantages over the diffeomorphic framework commonly employed in conventional studies. We first calculate the time-varying average of the mean shape development using a kernel regression technique with adaptive bandwidth. Then, eigenshape modes for every timepoint are calculated using principal component analysis with an additional regularization term that ensures the smoothness of the temporal change of the eigenshape modes. To further improve the performance, we applied data augmentation using a level set-based nonlinear morphing technique. The proposed algorithm was evaluated in the context of a spatiotemporal statistical shape modeling of a liver using 42 manually segmented livers from children whose age ranged from approximately 2 weeks to 95 months. Our method achieved a higher generalization and specificity ability compared with conventional methods.
影响因子：
0.7
作者：
Kishimoto M;Saito A;Takakuwa T;Yamada S;Matsuzoe H;Hontani H;Shimizu A,
通讯作者：
Shimizu A,