Semiautomated segmentation of lower extremity MRI reveals distinctive subcutaneous adipose tissue in lipedema: a pilot study

Abstract

Purpose: Lipedema is a painful subcutaneous adipose tissue (SAT) disease involving disproportionate SAT accumulation in the lower extremities that is frequently misdiagnosed as obesity. We developed a semiautomatic segmentation pipeline to quantify the unique lower-extremity SAT quantity in lipedema from multislice chemical-shift-encoded (CSE) magnetic resonance imaging (MRI).

Approach: Patients with lipedema ($n=15$) and controls ($n=13$) matched for age and body mass index (BMI) underwent CSE-MRI acquired from the thighs to ankles. Images were segmented to partition SAT and skeletal muscle with a semiautomated algorithm incorporating classical image processing techniques (thresholding, active contours, Boolean operations, and morphological operations). The Dice similarity coefficient (DSC) was computed for SAT and muscle automated versus ground truth segmentations in the calf and thigh. SAT and muscle volumes and the SAT-to-muscle volume ratio were calculated across slices for decades containing 10% of total slices per participant. The effect size was calculated, and Mann-Whitney $U$ test applied to compare metrics in each decade between groups (significance: two-sided $P<0.05$).

Results: Mean DSC for SAT segmentations was 0.96 in the calf and 0.98 in the thigh, and for muscle was 0.97 in the calf and 0.97 in the thigh. In all decades, mean SAT volume was significantly elevated in participants with versus without lipedema ($P<0.01$), whereas muscle volume did not differ. Mean SAT-to-muscle volume ratio was significantly elevated ($P<0.001$) in all decades, where the greatest effect size for distinguishing lipedema was in the seventh decade approximately midthigh ($r=0.76$).

Conclusions: The semiautomated segmentation of lower-extremity SAT and muscle from CSE-MRI could enable fast multislice analysis of SAT deposition throughout the legs relevant to distinguishing patients with lipedema from females with similar BMI but without SAT disease.

Keywords: body composition; chemical-shift-encoded magnetic resonance imaging; lipedema; musculoskeletal segmentation; whole-body magnetic resonance imaging.

Fig. 1

A female with a typical lipedema external presentation. Both anterior and posterior views show the appearance of columnar legs. Other features include skin dimpling (solid arrows) and ankle cuffing (dashed arrow). Though lipedema may have notable external features, patients can be misdiagnosed or mismanaged as obesity, motivating development of objective methods to differentiate lipedema.

Fig. 2

Schematic of automated segmentation. A mask of the leg boundaries is created using active contours segmentation of the in-phase image in step (1). Transverse slices of the water-weighted and fat-weighted images are masked by the leg boundary prior to classification of water and fat pixels in step (2a) and (2b), respectively. Classification is performed with adaptive thresholding with a custom-defined kernel size for each slice, based on the bounding box (red box) height ($H_{\mathrm{BOX}}$) in step (1). Morphological operations and flood-filling are applied to the water mask to label the skeletal muscle region in step (3) and produce a filled muscle mask. The fat mask is compartmentalized into marrow and IMAT and the SAT with a series of Boolean and morphological operations in step (4), to produce a final SAT mask. The marrow/IMAT structures are removed from the filled muscle mask in step (5) to produce a final muscle mask. Gray dotted arrows indicate steps where masks obtained from the other contrast were used. The top gray box delineates the source image contrasts (water-weighted, in-phase, and fat-weighted) used in segmentation, and the bottom gray box shows final masks (muscle and SAT) used for subsequent volume quantification.

Fig. 3

Global versus adaptive thresholding in extremity segmentation. (a) Signal intensity inhomogeneities (red arrows) can arise from poor $B_0$ shimming and signal drop off in regions farther from the scanner isocenter, which can be worsened in extremity scanning that has both a large in-plane and slice direction field of view. (b) Global thresholding techniques calculate an image histogram of signal intensities and determine a single threshold value that minimizes interclass variance between the foreground and background groups. However, the areas with signal drop off may be incorrectly classified as background pixels in the resulting segmentation (red arrows). (c) Adaptive thresholding can mitigate these false negative classifications by calculating a local threshold value for each pixel based on the mean intensity of a determined pixel neighborhood size. Applying the local threshold map produces a fat segmentation with improved foreground classification.

Fig. 4

Lower extremity MRI semiautomated image analysis pipeline. (a) CSE-MRI was acquired in stacks from head-to-ankles to produce fat- and water-weighted images. Axial slices of the lower extremities were selected between the upper thigh and ankle (red lines). Duplicate slices resulting from overlap of neighboring stacks were removed. (b) Pixels were classified as fat or water from the respective fat- and water-weighted images. Regions including bone marrow, IMAT, and skin were removed while preserving SAT and skeletal muscle segmentations. (c) SAT (yellow overlay) and muscle (red overlay) masks are overlaid on an axial fat-weighted image (blue box), and a coronal representation of the lower extremities.

Fig. 5

Decade-level comparison of imaging metrics. The mean SAT (left) and SAT-to-muscle volume ratio (right) for the control group (CN, hatch marks) and the lipedema group (LI, shaded) are visualized across 10 decades of the lower extremities. Both metrics were significantly higher ($P\le 0.01$) in all decades. †Indicates decade with largest effect size for each metric (nonparametric Wilcoxon test effect size, $r$).

Fig. 6

Representative example of (a) healthy female compared to a female patient with (b) lipedema. The healthy control is 28 years old with a BMI of $25.3\mathrm{kg}/{\mathrm{m}}^2$ and the patient with lipedema is 23 years old with a BMI of $25.1\mathrm{kg}/{\mathrm{m}}^2$. Structural measures of calf and thigh circumference are also similar (control versus lipedema values, calf circumference 37 cm versus 40 cm, thigh circumference 82 cm versus 86 cm). Lower extremity CSE-MRI reveals apparent thickened SAT in lipedema extending from thighs to ankles (yellow arrows). This is also observable on the transverse slice at the thigh level (blue box). SAT volume can be quantified by image analysis of musculoskeletal composition. In the midthigh region (decade 7): the mean SAT volume is greater for lipedema versus control 5.90 versus 3.89 mL, whereas mean muscle volume for lipedema versus control is 3.45 versus 4.16 mL. The SAT/muscle volume ratio in the midthigh is also greater in lipedema versus control 1.73 versus 0.94. Note: All stacks were rendered with an identical grayscale window/level, and image proportions were preserved.