Dear AuntMinnieEurope Member,
Many hospital managers have failed to learn the lessons of the 2017 WannaCry ransomware attack, it seems.
The findings from a new survey of 159 U.K. hospitals show that many of them lack sufficient in-house cybersecurity expertise, there is a wide imbalance in employee cybersecurity training and spending, and many facilities fail to meet training targets on information governance. Go to the Imaging Informatics Community, or click here.
Artificial intelligence holds great promise when it comes to coping with cyberthreats, but the technology can also be used to alter images. Israeli researchers elaborated on this topic at the RSNA 2018 meeting in Chicago. Find out more.
Breast cancer screening is making the headlines again. The National Health Service (NHS) in England is reviewing its approach to screening, and this has added momentum to the debate over how to optimize the service. Two long-term advocates of screening, Drs. László Tabár and Peter B. Dean, have given their opinion. Visit our Women's Imaging Community, or click here.
Compared with orthopedic surgeons and urologists, radiologists play relatively little golf, but at least they fare better than internal medicine physicians, according to a report in the BMJ that analyzes participation in golf among different medical specialties. Get the full story.
Emerging imaging technologies such as virtual reality (VR) and augmented reality (AR) have enhanced the visualization of medical images for a variety of purposes, from patient education to surgical simulation. Researchers from Strasbourg University Hospital in France assessed the numerous clinical applications of VR and AR, specifically for liver procedures. Learn more in the Advanced Visualization Community, or by clicking here.
The semifinal nominations for the EuroMinnies award scheme were announced yesterday. If you missed the announcement, you can view the list of seminfinalists. The finalists will be revealed in January, and the award presentations will take place at ECR 2019.
![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.auntminnieeurope.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)
