Validity of a new automated software program for visceral adipose tissue estimation

Abstract

Introduction: Given the considerable time and research cost of analyzing biomedical images to quantify adipose tissue volumes, automated image analysis methods are highly desirable. Hippo Fatt is a new software program designed to automatically quantify adipose tissue areas from magnetic resonance images without user inputs. Hippo Fatt has yet to be independently validated against commonly used image analysis software programs. Objective: Our aim was to compare estimates of VAT (visceral adipose tissue) and SAT (subcutaneous adipose tissue) using the new Hippo Fatt software against those from a widely used, validated, computer-assisted manual method (slice-O-matic version 4.2, Tomovision, Montreal, CA, USA) to assess its potential utility for large-scale studies. Methods: A Siemens Magnetom Vision 1.5-T whole-body scanner and a T1-weighted fast-spin echo pulse sequence were used to collect multiple, contiguous axial images of the abdomen from a sample of 40 healthy adults (20 men) aged 18-77 years of age, with mean body mass index of 29 kg/m 2 (range ¼ 19-43 kg/m 2 ). Results: Hippo Fatt provided estimates of VAT and SAT that were highly correlated with estimates using slice-O-matic (R 2 40.9). Average VAT was 9.4% lower and average SAT was 3.7% higher using Hippo Fatt compared to slice-O-matic; the overestimation of SAT tended to be greater among individuals with greater adiposity. Individual-level differences for VAT were also substantial; Hippo Fatt gave estimates of VAT ranging from 1184 cm 3 less to 566 cm 3 more than estimates for the same person using slice-O-matic. Conclusion: Hippo Fatt provides a rapid method of quantifying total VAT, although the method does not provide estimates that are interchangeable with slice-O-matic at either the group (mean) or individual level.

Accurate segmentation of subcutaneous and intermuscular adipose tissue from MR images of the thigh

“…formed by the HIPPO FAT software do not differ significantly from manual measurement performed by other validated tools, such as ANALYZE and SliceOmatic (13)(14)(15). Global values of SAT, IMAT, and muscle and bone volumes were evaluated for each subject.…”

“…Manual analysis of 3D datasets is time-consuming and operator-dependent, and thus automatic or semiautomatic computer assisted methods are highly desirable. Several automatic and semiautomatic image analysis algorithms were developed for abdominal fat distribution assessment (9 -12), and several studies provided comparison between available softwares (13)(14)(15). General-purpose semiautomated softwares as NIH ImageJ (14), Slice-O-Matic (15), and Analyze (14) are widely validated and allow delineation of fat deposits in different body locations, but may require a long processing time.…”

“…Several automatic and semiautomatic image analysis algorithms were developed for abdominal fat distribution assessment (9 -12), and several studies provided comparison between available softwares (13)(14)(15). General-purpose semiautomated softwares as NIH ImageJ (14), Slice-O-Matic (15), and Analyze (14) are widely validated and allow delineation of fat deposits in different body locations, but may require a long processing time. Automated methods show a good overall agreement with validated general-purpose softwares (14) and seem to have some distinct advantages over less automated methods, as there is less variability between evaluators and reduced analysis time (15).…”

FEM-based automatic segmentation of muscle and fat tissues from thoracic CT images

“…Further, we also estimated the tissue cross-sectional areas (cm 2 ) from segmentations by summing up tissue pixels and multiplying by the pixel surface area. In Table 2, we can see that the coefficient of variation (COV) between the automatic and manual methods is within (1 − 3)% which is similar or less than inter-or intra-operator COVs reported for manual segmentation [16].…”

Automatic segmentation of intra‐abdominal and subcutaneous adipose tissue in 3D whole mouse MRI

“…Automatic adipose segmentation has been developed for patient images (5)(6)(7)(8), but for rodents only manual or semi-automatic segmentation has been reported (4,9,10). The major difficulties in automating preclinical adipose segmentation are the increase in magnetic susceptibility artifacts around gas/tissue interfaces due to the higher magnetic fields commonly used, and the greater significance of partial volume effects (air, fat, or nonfat tissue occupying the same voxel) due to the small size of the subjects.…”