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

Jianan Wei1, Huawei Cai2, Yong Pi1

  • 1Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, 610065, People's Republic of China.

International Journal of Computer Assisted Radiology and Surgery
|March 13, 2022
PubMed
Summary

This study introduces an automated method for segmenting skeletons in whole-body bone scintigraphy (WBS), improving lesion localization and diagnostic accuracy in nuclear medicine.

Keywords:
Fully automatic methodImage registrationMedical image segmentationWhole-body bone scintigraphy

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Area of Science:

  • Nuclear Medicine Imaging
  • Medical Image Analysis
  • Computational Anatomy

Background:

  • Whole-body bone scintigraphy (WBS) is crucial for diagnosing bone lesions but manual segmentation is time-consuming and error-prone.
  • Accurate localization of bone lesions is vital for qualitative diagnosis in nuclear medicine.

Purpose of the Study:

  • To develop an automated, fine-grained skeleton segmentation method for WBS.
  • To address the challenges of manual segmentation in WBS, enhancing diagnostic efficiency and accuracy.

Main Methods:

  • A four-step approach involving novel denoising, gray probability-based restoration, histogram matching standardization, and registration-based segmentation.
  • Utilizes an atlas-based approach with deformation field calculation for precise segmentation mask generation.

Main Results:

  • The proposed method significantly improved image quality metrics compared to traditional registration (Morphon).
  • Demonstrated reductions in mean square error and increases in peak signal-to-noise ratio and mean structural similarity.

Conclusions:

  • The developed method achieves robust and fine-grained skeleton segmentation for WBS.
  • This fully automated approach shows promise for clinical applications in nuclear medicine, improving lesion detection and diagnosis.