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Automatic vertebral column extraction by whole-body bone SPECT scan

  • Sheng Fang Huang*
  • , Hao Yu Chao
  • , Pan Fu Kao
  • , Wei Chih Shen
  • , Yu Hsiang Chou
  • , Shu Hsin Liu
  • *Corresponding author for this work
  • Tzu Chi University
  • Chung Shan Medical University
  • Asia University Taiwan
  • Buddhist Tzu-Chi General Hospital Taiwan
  • Tzu Chi University of Science and Technology

Research output: Contribution to journalJournal Article peer-review

1 Scopus citations

Abstract

Bone extraction and division can enhance the accuracy of diagnoses based on whole-body bone SPECT data. This study developed a method for using conventional SPECT for automatic recognition of the vertebral column. A novel feature of the proposed approach is a novel "bone graph" image description method that represents the connectivity between these image regions to facilitate manipulation of morphological relationships in the skeleton before surgery. By tracking the paths shown on the bone graph, skeletal structures can be identified by performing morphological operations. The performance of the method was evaluated quantitatively and qualitatively by two experienced nuclear medicine physicians. Datasets for whole-body bone SPECT scans in 46 lung cancer patients with bone metastasis were obtained with Tc-99m MDP. The algorithm successfully segmented vertebrae in the thoracolumbar spine. The quantitative assessment shows that the segmentation method achieved an average TP, FP, and FN rates of 95.1%, 9.1%, and 4.9%. The qualitative evaluation shows an average acceptance rate of 83%, where the data for the acceptable and unacceptable groups had a Cronbach's alpha value of 0.718, which indicated reasonable internal consistency and reliability.

Original languageEnglish
Article number647548
JournalComputational and Mathematical Methods in Medicine
Volume2013
DOIs
StatePublished - 2013
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

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