Fully automated large-scale assessment of visceral and subcutaneous abdominal adipose tissue by magnetic resonance imaging

Abstract

Objective: To describe and evaluate a fully automated method for characterizing abdominal adipose tissue from magnetic resonance (MR) transverse body scans. Methods: Four MR pulse sequences were applied: SE, FLAIR, STIR, and FRFSE. On 39 subjects, each abdomen was traversed by 15 contiguous transaxial images. The total abdominal adipose tissue (TAAT) was calculated from thresholds obtained by slice histogram analysis. The same thresholds were also used in the manual volume calculation of TAAT, subcutaneous abdominal adipose tissue (SAAT) and visceral abdominal adipose tissue (VAAT). Image segmentation methods, including edge detection, mathematical morphology, and knowledge-based curve fitting, were used to automatically separate SAAT from VAAT in various 'nonstandard' cases such as those with heterogeneous magnetic fields and movement artefacts. Results: The percentage root mean squared errors of the method for SAAT and VAAT ranged from 1.0 to 2.7% for the four sequences. It took approximately 7 and 15 min to complete the 15-slice volume estimation of the three adipose tissue classes using automated and manual methods, respectively. Conclusion:The results demonstrate that the proposed method is robust and accurate. Although the separation of SAAT and VAAT is not always perfect, this method could be especially helpful in dealing with large amounts of data such as in epidemiological studies.

Quantitative comparison and evaluation of software packages for assessment of abdominal adipose tissue distribution by magnetic resonance imaging

“…TAT was calculated by adding SAT and VAT (TAT ¼ SAT þ VAT); therefore, some small intramuscular deposits of adipose tissue were not accounted for. Similar to other published reports, 5,7,18,22,25,29,[33][34][35][36][37][40][41][42][43][44][45]47,[49][50][51][52][53][54][55][56][57][58][59][60][61][62][63] VAT and SAT were not further subdivided.…”

“…At the time this project started, only a few fully automated approaches had been published and had been made available. Recently published algorithms, like the one developed by Liou et al 37 Figure 5 Scatter plot showing the inter-test comparison of visceral adipose tissue (VAT) in 10 cm 2 using the average over two measurements obtained by observer 1 with Analyze, HippoFat, EasyVision and SliceOmatic compared to the averaged results obtained using NIHImage.…”

“…14,17,[34][35][36][37][38][39] Most approaches are composed of two steps: in the first step, based on the histogram a threshold between adipose and lean tissues is determined manually or automatically and, in the second step, the areas of SAT and VAT are delineated. The segmentation routine can also include data pre-processing steps like de-noising and smoothing of the MR images to facilitate segmentation.…”

“…[34][35][36][37] Positano et al [34][35][36] proposed a fuzzy c-means approach. An active contour algorithm was applied to identify and estimate the volumes of VAT and SAT.…”

“…An active contour algorithm was applied to identify and estimate the volumes of VAT and SAT. Liou et al 37 determined a threshold slice by slice based on the average signal intensity of the imaged slice. The segmentation consisted of a four-step procedure in which masks for the body contour, non-fat tissue, TAT, VAT and SAT were applied to determine the tissue boundaries.…”