Multi-Layer Deformable Models for medical image segmentation
Multi-Layer Deformable Models for medical image segmentation
复制标题
用于医学图像分割的多层可变形模型
DOI：
10.1109/itab.2010.5687695
复制
发表时间：
2010
期刊：
影响因子：
--
通讯作者：
S. Wesarg
中科院分区：
文献类型：
--
作者：
Marius Erdt;Patrice Schlegel;S. Wesarg
提出了一种用于医学图像分割的多层可变形模型（MLDM）。与常见的基于可变形模型的分割方法相比，我们的新方法采用了多层几何模型，可以对器官内部进行采样。自适应逻辑处理从内层获得的附加信息，以便将模型拟合到数据中。为了使模型能够准确地适应空腔，将变形与由面向连杆的柔性表示的动态内能函数耦合在一起。利用额外的深度信息，我们的方法可以更可靠地检测器官之间的低对比度转换，并且比现有方法更好地从错误的模型初始化中恢复。我们的方法已经通过肝脏和膀胱CT扫描的代表性CT数据集进行了评估。使用地面真值数据的评估表明，与普通的单表面分割相比，我们的多层技术产生了更好的结果。由于层数是灵活的，因此可以将携带较少区域信息的最内部区域排除在优化之外。结合MLDM优化的线性特性，我们的方法在速度方面优于其他体积分割方法。
In this work, a Multi-Layer Deformable Model (MLDM) for medical image segmentation is proposed. In contrast to common deformable model based segmentation approaches our new method incorporates a multi-layer geometric model that allows a sampling of the organ's interior. An adaptation logic processes the additional information gained from interior layers in order to fit the model to the data. The deformation is coupled with a dynamic internal energy function represented by a link-oriented flexibility in order to allow the model to accurately adapt to cavities. Exploiting the additional depth information, our approach detects low contrasted transitions between organs more reliably and recovers better from bad model initialization than existing methods. Our approach has been evaluated using representative CT data sets of the liver as well as CT bladder scans. Evaluation using ground truth data showed that our multi-layer technique yields superior results in contrast to common single surface segmentation. Since the amount of layers is flexible, the most interior regions which only carry little regional information can be excluded from optimization. Together with the linear nature of MLDM optimization our approach outperforms other volumetric segmentation methods in terms of speed.