Automatic quantification of subcutaneous and visceral adipose tissue from whole-body magnetic resonance images suitable for large cohort studies.

PURPOSE

To develop an automated method with which to distinguish metabolically different adipose tissues in a large number of subjects using whole-body magnetic resonance imaging (MRI) datasets for improving the understanding of chronic disease risk predictions associated with distinct adipose tissue compartments.

MATERIALS AND METHODS

In all, 314 participants were scanned using a 1.5T MRI-scanner with a 2-point Dixon whole-body sequence. Image segmentation was automated using standard image processing techniques and knowledge-based methods. Abdominal adipose tissue was separated into subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) by statistical shape models. Bone marrow was removed to provide a more accurate measurement of adipose tissue. To assess segmentation accuracy, ground-truth segmentations in 52 images were performed manually by one operator. Due to the high effort of manual delineation, manual segmentation was limited to seven slices per volume.

RESULTS

Volumetric differences were 3.30 ± 2.97% and 6.22 ± 5.28% for SAT and VAT, respectively. The systematic error shows an overestimation of 4.22 ± 7.01% for VAT and 0.37 ± 4.45% for SAT. Coefficients-of-variation from repeated measurements were: 3.50 ± 2.93% for VAT and 0.35 ± 0.26% for SAT. The approach of removing bone marrow worked well in most body regions. Only occasionally the method failed for knees and/or shinbone, which resulted in an overestimation of SAT by 3.14 ± 1.45%.

CONCLUSION

We developed a fully automatic process to assess SAT and VAT in whole-body MRI data. The method can support epidemiological studies investigating the relationship between excess body fat and chronic diseases.