PurposeLipedema 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).ApproachPatients 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).ResultsMean 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).ConclusionsThe 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.
Lipedema is a painful connective tissue disease involving excessive subcutaneous adipose tissue (SAT) accumulation in the lower extremities. Lipedema remains poorly recognized as a unique clinical entity and is often misdiagnosed as obesity. Whole-body magnetic resonance imaging (MRI) acquisitions could provide insight into the unique body composition of lipedema, yet methodologies for multi-slice analyses are lacking. In this work, a semi-automated processing workflow was developed to segment and quantify adiposity from whole-leg chemical-shift encoded (CSE) MRI to distinguish lipedema. Patients with lipedema (N=15) and controls (N=13) matched for age and body mass index underwent a CSE MRI exam in eight stacks from the head-to-ankles. Slices from thighs-to-ankles were segmented via Chan-Vese segmentation, clustering, and morphological techniques to separate SAT and skeletal muscle. SAT and muscle volume per slice and the SAT-to-muscle volume ratio were recorded in decades of slices and compared between groups using Mann-Whitney U test with two-sided significance criteria p<0.05. SAT volume was significantly elevated in participants with lipedema in all decades (p<0.001), while muscle volume was not significantly different. SAT-to-muscle volume ratio was elevated in lipedema compared to controls (p<0.001), with the greatest effect size (rrb = 0.74) observed in the eighth decade corresponding to the mid-thigh region. These findings reveal SAT distribution is uniquely elevated throughout the legs of participants with lipedema as discerned from whole-leg CSE MRI. CSE MRI and analysis methods developed herein for SAT quantification could inform the diagnosis of lipedema, which suffers from few objective strategies to differentiate the disease from obesity.
During laparoscopic surgery, insufflation introduces significant deformations to the liver through reshaping the abdominal cavity. Additionally, insufflation is associated with elevation of portal and hepatic venous pressures causing hemodynamic changes that may cause swelling or engorgement of the liver. Current methods for elastic soft tissue registration cannot account for these secondary effects, which have yet remained largely neglected. This paper presents preliminary work towards modeling these intraoperative physiological changes caused by insufflation to improve the accuracy of image-to-physical registration during laparoscopic procedures. Spatially localized modes of internal pressure-driven deformations are introduced to allow reconstruction of intraoperative changes to intrahepatic pressure gradients during registration. A pilot experiment was performed in one sheep to measure volumetric tissue changes induced by insufflation. Liver volume increased by 6.3 ± 0.7% between insufflated and preoperative states, according to three independent raters. Registrations were also performed between organ states, utilizing: (1) rigid registration, (2) deformable registration based on application of external forces to the organ, and (3) the same deformable registration extended to incorporate displacements driven by changes to intrahepatic pressure gradients. Target registration error (TRE) decreased from 15.2 ± 3.2 mm with rigid registration to 5.0 ± 1.9 mm with reconstruction of external boundary forces. When incorporating internal pressure changes, TRE further decreased to 3.0 ± 1.2 mm. Additional analysis comparing a pure scaling approach with the proposed mechanics-based method to account for volume change demonstrated that the method incorporating intrahepatic pressure changes leads to more accurate image-to-physical registrations between the preoperative and insufflated liver.
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