Segmentation and Visual Analysis of Whole-Body Mouse Skeleton microSPECT

被引:7
|
作者
Khmelinskii, Artem [1 ]
Groen, Harald C. [2 ]
Baiker, Martin [1 ]
de Jong, Marion [2 ,3 ]
Lelieveldt, Boudewijn P. F. [1 ,4 ]
机构
[1] Leiden Univ, Div Image Proc, Dept Radiol, Med Ctr, Leiden, Netherlands
[2] Erasmus MC, Dept Nucl Med, Rotterdam, Netherlands
[3] Erasmus MC, Dept Radiol, Rotterdam, Netherlands
[4] Delft Univ Technol, Dept Intelligent Syst, Delft, Netherlands
来源
PLOS ONE | 2012年 / 7卷 / 11期
关键词
REGISTRATION;
D O I
10.1371/journal.pone.0048976
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Whole-body SPECT small animal imaging is used to study cancer, and plays an important role in the development of new drugs. Comparing and exploring whole-body datasets can be a difficult and time-consuming task due to the inherent heterogeneity of the data (high volume/throughput, multi-modality, postural and positioning variability). The goal of this study was to provide a method to align and compare side-by-side multiple whole-body skeleton SPECT datasets in a common reference, thus eliminating acquisition variability that exists between the subjects in cross-sectional and multi-modal studies. Six whole-body SPECT/CT datasets of BALB/c mice injected with bone targeting tracers Tc-99m-methylene diphosphonate (Tc-99m-MDP) and Tc-99m-hydroxymethane diphosphonate (Tc-99m-HDP) were used to evaluate the proposed method. An articulated version of the MOBY whole-body mouse atlas was used as a common reference. Its individual bones were registered one-by-one to the skeleton extracted from the acquired SPECT data following an anatomical hierarchical tree. Sequential registration was used while constraining the local degrees of freedom (DoFs) of each bone in accordance to the type of joint and its range of motion. The Articulated Planar Reformation (APR) algorithm was applied to the segmented data for side-by-side change visualization and comparison of data. To quantitatively evaluate the proposed algorithm, bone segmentations of extracted skeletons from the correspondent CT datasets were used. Euclidean point to surface distances between each dataset and the MOBY atlas were calculated. The obtained results indicate that after registration, the mean Euclidean distance decreased from 11.5 +/- 12.1 to 2.6 +/- 2.1 voxels. The proposed approach yielded satisfactory segmentation results with minimal user intervention. It proved to be robust for "incomplete'' data (large chunks of skeleton missing) and for an intuitive exploration and comparison of multi-modal SPECT/CT cross-sectional mouse data.
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页数:10
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