Atlas-based articulated skeleton segmentation of μSPECT mouse data

In this paper we propose an automated articulated atlas-based approach for bone segmentation in whole-body μSPECT data of mice, obtained by injecting the ^99mTc-methylene diphosphonate (^99mTc - MDP). This is a difficult task, since SPECT data is usually noisy and low resolution, and the skeleton image is incomplete with several portions missing (e.g.: in limbs and skull). For this purpose the articulated version of the MOBY atlas skeleton with a correspondent hierarchical tree description is used. Iterative Closest Point registration is used, while constraining the local degrees of freedom (DoFs) in accordance to the type of joint and its range of motion. The method was tested using 3 whole-body μSPECT mouse datasets acquired using a NanoSPECT/CT scanner for small animals and the MOBY atlas. To evaluate the proposed algorithm, manual bone segmentations of extracted skeletons from the correspondent CT datasets were used. Euclidean point to surface distances for each dataset and the MOBY atlas were calculated. The obtained results indicate that after registration, the mean Euclidean distance descreased from 8.37 ± 8.70 to 2.27 ± 2.06 voxels. The results were presented using a novel method for change visualization in small animal imaging (Articulated Planar Reformation).