Abstract Information about the size of a tumor and its temporal evolution is needed for
diagnosis as well as treatment of brain tumor patients. The aim of the study was to …

Background Automated brain tumor segmentation methods are computational algorithms
that yield tumor delineation from, in this case, multimodal magnetic resonance imaging …

Uncertainty measures of medical image analysis technologies, such as deep learning, are
expected to facilitate their clinical acceptance and synergies with human expertise …

Background This study aims to identify robust radiomic features for Magnetic Resonance
Imaging (MRI), assess feature selection and machine learning methods for overall survival …

Deep learning for regression tasks on medical imaging data has shown promising results.
However, compared to other approaches, their power is strongly linked to the dataset size. In …

Publicly available Glioblastoma (GBM) datasets predominantly include pre-operative
Magnetic Resonance Imaging (MRI) or contain few follow-up images for each patient …

OBJECTIVE In the treatment of glioblastoma, residual tumor burden is the only prognostic
factor that can be actively influenced by therapy. Therefore, an accurate, reproducible, and …

MRSI grids frequently show spectra with poor quality, mainly because of the high sensitivity
of MRS to field inhomogeneities. These poor quality spectra are prone to quantification …

Objective Comparison of a fully-automated segmentation method that uses compartmental
volume information to a semi-automatic user-guided and FDA-approved segmentation …

Introduction Anaplastic pilocytic astrocytoma (APA) is an exceptionally rare type of high-
grade glioma in adults. Establishing histopathological diagnosis is challenging and its …