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 …

Purpose To improve the robustness of deep learning–based glioblastoma segmentation in a
clinical setting with sparsified datasets. Materials and Methods In this retrospective study …

Purpose Accurate brain tumor segmentation on magnetic resonance imaging (MRI) has
wide-ranging applications such as radiosurgery planning. Advances in artificial intelligence …

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

This study tests the generalisability of three Brain Tumor Segmentation (BraTS) challenge
models using a multi-center dataset of varying image quality and incomplete MRI datasets …

Purpose To design a computational method for automatic brain glioma segmentation of
multimodal MRI scans with high efficiency and accuracy. Materials and Methods The 2018 …

Automatic segmentation of brain tumors is an essential but challenging step for extracting
quantitative imaging biomarkers for accurate tumor detection, diagnosis, prognosis …

Purpose To present results from a literature survey on practices in deep learning
segmentation algorithm evaluation and perform a study on expert quality perception of brain …

Background and purpose Convolutional neural networks (CNN s) are commonly used for
segmentation of brain tumors. In this work, we assess the effect of cross‐institutional training …

The quantitative analysis of images acquired in the diagnosis and treatment of patients with
brain tumors has seen a significant rise in the clinical use of computational tools. The …