An automatic fine-grained skeleton segmentation method for whole-body bone scintigraphy using atlas-based registration

• Published in: International journal for computer assisted radiology and surgery 2022-04, Vol.17 (4), p.673-681
• Main Authors: Wei, Jianan, Cai, Huawei, Pi, Yong, Zhao, Zhen, Yi, Zhang
• Format: Article
• Language: English
• Subjects: Algorithms; Bone and Bones - diagnostic imaging; Computer Imaging; Computer Science; Health Informatics; Histograms; Humans; Image Processing, Computer-Assisted - methods; Image segmentation; Imaging; Lesions; Medical imaging; Medicine; Medicine & Public Health; Nuclear medicine; Original Article; Pattern Recognition and Graphics; Physicians; Radioisotopes; Radiology; Radionuclide Imaging; Registration; Scintigraphy; Signal to noise ratio; Surgery; Vision; Whole Body Imaging - methods
• ISSN: 1861-6429; 1861-6410; 1861-6429
• DOI: 10.1007/s11548-022-02579-2

Purpose 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. Method 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. Results The experimental results show that the proposed method outperforms the traditional registration (Morphon): mean square error decreased from 11.14 × 10 - 3 to 2.10 × 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. Conclusions 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.