The aim of the workshop is to provide wide-ranging coverage of all the main liver pathologies that will be presented by means of an integrated multimodality approach. Imaging findings will be analyzed on the basis of their pathological correlations. The most relevant technological advances of different modalities and contrast media applied to liver imaging will be highlighted. It is intended to equip delegates with a better understanding of segmental anatomy of the liver in order to share a standard radiological and surgical terminology. Also, great emphasis will be put on giving practical suggestions on the diagnostic workup of liver diseases in the daily practice. The format of the workshop will include formal lectures that have been assigned to expert colleagues able to thoroughly discuss each topic. Moreover, an interactive discussion on real clinical cases will be led by the faculty members who will help to define the state-of-the-art in the diagnosis of liver diseases.
6th European Society of Gastrointestinal and Abdominal Radiology (ESGAR) Liver Imaging Workshop
Apr 28th, 2010Apr 29th, 2010
Barcelona, --
ES
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![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&fit=crop&h=112&q=70&w=112)





