Automatic Measurement of the Total Visceral Adipose Tissue From Computed Tomography Images by Using a Multi-Atlas Segmentation Method

J Comput Assist Tomogr. 2018 Jan/Feb;42(1):139-145. doi: 10.1097/RCT.0000000000000652.


Background: The visceral adipose tissue (VAT) volume is a predictive and/or prognostic factor for many cancers. The objective of our study was to develop an automatic measurement of the whole VAT volume using a multi-atlas segmentation (MAS) method from a computed tomography.

Methods: A total of 31 sets of whole-body computed tomography volume data were used. The reference VAT volume was defined on the basis of manual segmentation (VATMANUAL). We developed an algorithm, which measured automatically the VAT volumes using a MAS based on a nonrigid volume registration algorithm coupled with a selective and iterative method for performance level estimation (SIMPLE), called VATMAS_SIMPLE. The results were evaluated using intraclass correlation coefficient and dice similarity coefficients.

Results: The intraclass correlation coefficient of VATMAS_SIMPLE was excellent, at 0.976 (confidence interval, 0.943-0.989) (P < 0.001). The dice similarity coefficient of VATMAS_SIMPLE was also good, at 0.905 (SD, 0.076).

Conclusions: This newly developed algorithm based on a MAS can measure accurately the whole abdominopelvic VAT.

MeSH terms

  • Algorithms
  • Atlases as Topic
  • Female
  • Humans
  • Intra-Abdominal Fat / diagnostic imaging*
  • Male
  • Software
  • Tomography, X-Ray Computed / methods*