Khalifa Fahmi, Soliman Ahmed, Elmaghraby Adel, Gimel'farb Georgy, El-Baz Ayman
Bioengineering Department, University of Louisville, Louisville, KY, USA; Electronics and Communication Engineering Department, Mansoura University, Mansoura, Egypt.
Bioengineering Department, University of Louisville, Louisville, KY, USA.
Comput Math Methods Med. 2017;2017:9818506. doi: 10.1155/2017/9818506. Epub 2017 Feb 9.
Kidney segmentation is an essential step in developing any noninvasive computer-assisted diagnostic system for renal function assessment. This paper introduces an automated framework for 3D kidney segmentation from dynamic computed tomography (CT) images that integrates discriminative features from the current and prior CT appearances into a random forest classification approach. To account for CT images' inhomogeneities, we employ discriminate features that are extracted from a higher-order spatial model and an adaptive shape model in addition to the first-order CT appearance. To model the interactions between CT data voxels, we employed a higher-order spatial model, which adds the triple and quad clique families to the traditional pairwise clique family. The kidney shape prior model is built using a set of training CT data and is updated during segmentation using not only region labels but also voxels' appearances in neighboring spatial voxel locations. Our framework performance has been evaluated on in vivo dynamic CT data collected from 20 subjects and comprises multiple 3D scans acquired before and after contrast medium administration. Quantitative evaluation between manually and automatically segmented kidney contours using Dice similarity, percentage volume differences, and 95th-percentile bidirectional Hausdorff distances confirms the high accuracy of our approach.
肾脏分割是开发任何用于肾功能评估的非侵入性计算机辅助诊断系统的关键步骤。本文介绍了一种用于从动态计算机断层扫描（CT）图像中进行三维肾脏分割的自动化框架，该框架将当前和先前CT表现中的判别特征集成到随机森林分类方法中。为了解决CT图像的不均匀性问题，除了一阶CT表现外，我们还采用了从高阶空间模型和自适应形状模型中提取的判别特征。为了对CT数据体素之间的相互作用进行建模，我们采用了高阶空间模型，该模型在传统的成对团块家族中增加了三元和四元团块家族。肾脏形状先验模型是使用一组训练CT数据构建的，并在分割过程中不仅使用区域标签，还使用相邻空间体素位置中的体素外观进行更新。我们的框架性能已在从20名受试者收集的体内动态CT数据上进行了评估，这些数据包括在注射造影剂前后采集的多次三维扫描。使用骰子相似度、体积百分比差异和第95百分位数双向豪斯多夫距离对手动和自动分割的肾脏轮廓进行定量评估，证实了我们方法的高精度。
