Crump Institute of Molecular Imaging, David Geffen School of Medicine, University of California, Los Angeles, CA 90066, USA.
IEEE Trans Med Imaging. 2012 Jan;31(1):88-102. doi: 10.1109/TMI.2011.2165294. Epub 2011 Aug 18.
Micro-CT is widely used in preclinical studies of small animals. Due to the low soft-tissue contrast in typical studies, segmentation of soft tissue organs from noncontrast enhanced micro-CT images is a challenging problem. Here, we propose an atlas-based approach for estimating the major organs in mouse micro-CT images. A statistical atlas of major trunk organs was constructed based on 45 training subjects. The statistical shape model technique was used to include inter-subject anatomical variations. The shape correlations between different organs were described using a conditional Gaussian model. For registration, first the high-contrast organs in micro-CT images were registered by fitting the statistical shape model, while the low-contrast organs were subsequently estimated from the high-contrast organs using the conditional Gaussian model. The registration accuracy was validated based on 23 noncontrast-enhanced and 45 contrast-enhanced micro-CT images. Three different accuracy metrics (Dice coefficient, organ volume recovery coefficient, and surface distance) were used for evaluation. The Dice coefficients vary from 0.45 ± 0.18 for the spleen to 0.90 ± 0.02 for the lungs, the volume recovery coefficients vary from 0.96 ± 0.10 for the liver to 1.30 ± 0.75 for the spleen, the surface distances vary from 0.18 ± 0.01 mm for the lungs to 0.72 ± 0.42 mm for the spleen. The registration accuracy of the statistical atlas was compared with two publicly available single-subject mouse atlases, i.e., the MOBY phantom and the DIGIMOUSE atlas, and the results proved that the statistical atlas is more accurate than the single atlases. To evaluate the influence of the training subject size, different numbers of training subjects were used for atlas construction and registration. The results showed an improvement of the registration accuracy when more training subjects were used for the atlas construction. The statistical atlas-based registration was also compared with the thin-plate spline based deformable registration, commonly used in mouse atlas registration. The results revealed that the statistical atlas has the advantage of improving the estimation of low-contrast organs.
微计算机断层扫描（Micro-CT）广泛应用于小动物的临床前研究。由于典型研究中软组织对比度低，因此从非对比增强的微计算机断层扫描图像中分割软组织器官是一个具有挑战性的问题。在这里，我们提出了一种基于图谱的方法来估计小鼠微计算机断层扫描图像中的主要器官。基于 45 个训练对象构建了主要躯干器官的统计图谱。使用统计形状模型技术包括了个体间的解剖学变化。使用条件高斯模型描述了不同器官之间的形状相关性。对于配准，首先通过拟合统计形状模型来注册微计算机断层扫描图像中的高对比度器官，然后使用条件高斯模型从高对比度器官中估计低对比度器官。基于 23 个非对比增强和 45 个对比增强的微计算机断层扫描图像验证了注册的准确性。使用三种不同的精度度量（Dice 系数、器官体积恢复系数和表面距离）进行评估。Dice 系数从脾脏的 0.45±0.18 到肺脏的 0.90±0.02 不等，体积恢复系数从肝脏的 0.96±0.10 到脾脏的 1.30±0.75 不等，表面距离从肺脏的 0.18±0.01 毫米到脾脏的 0.72±0.42 毫米不等。与两个公开的单个体小鼠图谱（即 MOBY 幻影和 DIGIMOUSE 图谱）相比，统计图谱的注册精度更高。为了评估训练对象数量对配准精度的影响，使用不同数量的训练对象进行图谱构建和注册。结果表明，当使用更多的训练对象进行图谱构建时，注册精度会提高。还将基于统计图谱的配准与常用于小鼠图谱配准的薄板样条变形配准进行了比较。结果表明，统计图谱具有提高低对比度器官估计的优势。
