Simple Summary Current auto-segmentation methods for glioblastoma utilize mainly pre-
operative 1.5 T and 3T MRI. The first commercial MRI-linear accelerator (linac) radiation …

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

Simple Summary With several previous efforts to segment pre-surgical brain tumor lesions
from MRI, we sought to shine a different light on the problem. Radiation treatment planning …

Purpose/Objective (s) Recently identified changes in brain during daily MRI-guided radiation
therapy (MRgRT) of patients with glioblastoma (GBM) can permit adaptive radiotherapy …

Objective. To develop a deep ensemble learning (DEL) model with radiomics spatial
encoding execution for improved glioma segmentation accuracy using multi-parametric …

A precise radiotherapy plan is crucial to ensure accurate segmentation of glioblastomas
(GBMs) for radiation therapy. However, the traditional manual segmentation process is labor …

Background Measurement of volumetric features is challenging in glioblastoma. We
investigate whether volumetric features derived from preoperative MRI using a convolutional …

Purpose Most radiomic studies use the features extracted from the manually drawn tumor
contours for classification or survival prediction. However, large interobserver segmentation …

Objectives The aims of this study were, first, to evaluate a deep learning–based, automatic
glioblastoma (GB) tumor segmentation algorithm on clinical routine data from multiple …

Background and Purpose Reproducible segmentation of brain tumors on magnetic
resonance images is an important clinical need. This study was designed to evaluate the …