Whole-body tumor segmentation from FDG-PET/CT: Leveraging a segmentation prior from tissue-wise projections.

: Accurate tumor detection and quantification are important for optimized therapy planning and evaluation. Total tumor burden is also an appealing biomarker for clinical trials. Manual examination and annotation of oncologic PET/CT is labor-intensive and demands a high level of expertise.

Predicting the Antifungal Activity of Small Organic Compounds on Mold using Molecular Dynamics Simulations.

Atomistic models of the plasma membrane of the pathogenic mold are developed. These models are described with an empirical molecular mechanical (MM) force field in combination with molecular dynamics (MD) simulations. The solvated plasma membrane models are brought into contact with 35 small organic compounds to observe their impact on a variety of membrane properties.

A detailed analysis of body composition in relation to cardiopulmonary exercise test indices.

A cardiopulmonary exercise test (CPET) is a test assessing an individual's physiological response during exercise. Results may be affected by body composition, which is best evaluated through imaging techniques like magnetic resonance imaging (MRI). The aim of this study was to assess relationships between body composition and indices obtained from CPET.

Deep learning-based dose prediction for magnetic resonance-guided prostate radiotherapy.

Background: Daily adaptive radiotherapy, as performed with the Elekta Unity MR-Linac, requires choosing between different adaptation methods, namely ATP (Adapt to Position) and ATS (Adapt to Shape), where the latter requires daily re-contouring to obtain a dose plan tailored to the daily anatomy. These steps are inherently resource-intensive, and quickly predicting the dose distribution and the dosimetric evaluation criteria while the patient is on the table could facilitate a fast selection of adaptation method and decrease the treatment times.

Purpose: In this work, we aimed to develop a deep-learning-based dose-prediction pipeline for prostate MR-Linac treatments.

Classification of rotation-invariant biomedical images using equivariant neural networks.

Transmission electron microscopy (TEM) is an imaging technique used to visualize and analyze nano-sized structures and objects such as virus particles. Light microscopy can be used to diagnose diseases or characterize e.g.