Brian Casey, Erik L. Ridley, Kate Madden Yee, and Wayne Forrest[email protected]HomeWinners of 20th annual Minnies are radiology's bestMinnies winners, page 3October 22, 2019HomeWinners of 20th annual Minnies are radiology's bestMinnies winners, page 2October 22, 2019HomeWinners of 20th annual Minnies are radiology's bestFor two decades, the annual Minnies awards have served as a barometer of excellence in radiology. This year's winners are no exception, demonstrating the amazing achievements being made by researchers, educators, vendors, and software developers across medical imaging.October 21, 2019Page 1 of 1Top StoriesDigital X-RayLLMs excel at scoliosis detection on spine x-raysFor scoliosis detection, Grok 4 led with 94.2% accuracy.HomeExpanding opportunities for interventional radiology practiceCTWelding fumes linked to distinct lung CT changes, functionMolecular ImagingPSMA-PET linked to higher use of systemic therapiesSponsor ContentThe Wedding Was Easy. The Marriage Is the Work.
![Overview of the study design. (A) The fully automated deep learning framework was developed to estimate body composition (BC) (defined as subcutaneous adipose tissue [SAT] in liters; visceral adipose tissue [VAT] in liters; skeletal muscle [SM] in liters; SM fat fraction [SMFF] as a percentage; and intramuscular adipose tissue [IMAT] in deciliters) from MRI. The fully automated framework comprised one model (model 1) to quantify different BC measures (SAT, VAT, SM, SMFF, and IMAT) as three-dimensional (3D) measures from whole-body MRI scans. The second model (model 2) was trained to identify standardized anatomic landmarks along the craniocaudal body axis (z coordinate field), which allowed for subdividing the whole-body measures into different subregions typically examined on clinical routine MRI scans (chest, abdomen, and pelvis). (B) BC was quantified from whole-body MRI in over 66,000 individuals from two large population-based cohort studies, the UK Biobank (UKB) (36,317 individuals) and the German National Cohort (NAKO) (30,291 individuals). Bar graphs show age distribution by sex and cohort. BMI = body mass index. (C) After the performance assessment of the fully automated framework, the change in BC measures, distributions, and profiles across age decades were investigated. Age-, sex-, and height-adjusted body composition reference curves were calculated and made publicly available in a web-based z-score calculator (https://circ-ml.github.io).](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)
