Title
An automatic fine-grained skeleton segmentation method for whole-body bone scintigraphy using atlas-based registration
Abstract
Whole-body bone scintigraphy (WBS) is one of the common imaging methods in nuclear medicine. It is a time-consuming, tedious, and error-prone issue for physicians to determine the location of bone lesions which is important for the qualitative diagnosis of bone lesions. In this paper, an automatic fine-grained skeleton segmentation method for WBS is developed. The proposed method contains four steps. In the first step, a novel denoising method is proposed to remove the noise from WBS which benefits the location of the skeleton. In the second step, a restoration method based on gray probability distribution is developed to repair the partial contamination caused by the high local density of radionuclide. Then, the standardization for WBS is performed by the exact histogram matching. Finally, the deformation field between the atlas and the input WBS is calculated by registration, and the segmentation mask of the input WBS is obtained by wrapping the segmentation mask of the atlas with the deformation field. The experimental results show that the proposed method outperforms the traditional registration (Morphon): mean square error decreased from $$11.14 \times 10^{-3}$$ to $$2.10 \times 10^{-3}$$ , peak signal-to-noise ratio increased from 21.26 to 26.92, and mean structural similarity increased from 0.9986 to 0.9998. Our experiments show that the proposed method can achieve robust and fine-grained results which outperform the traditional registration method, indicating it could be helpful in clinical application. To the best of our knowledge, this is the first work that implements a fully automated fine-grained skeleton segmentation method for WBS.
Year
DOI
Venue
2022
10.1007/s11548-022-02579-2
International Journal of Computer Assisted Radiology and Surgery
Keywords
DocType
Volume
Image registration, Medical image segmentation, Whole-body bone scintigraphy, Fully automatic method
Journal
17
Issue
ISSN
Citations 
4
1861-6429
0
PageRank 
References 
Authors
0.34
4
5
Name
Order
Citations
PageRank
Jianan Wei100.34
Huawei Cai200.34
Yong Pi3163.06
Zhen Zhao400.34
Zhang Yi535637.14