BACKGROUND AND PURPOSE: Malignant melanoma is an aggressive skin cancer in
which brain metastases are common. Our aim was to establish and evaluate a deep …

Objectives To develop and validate a two-stage deep learning model for automatic detection
and segmentation of brain metastases (BMs) in MRI images. Methods In this retrospective …

Background Manual detection of brain metastases is both laborious and inconsistent, driving
the need for more efficient solutions. Accordingly, our systematic review and meta-analysis …

The purpose of this study was to assess the clinical value of a deep learning (DL) model for
automatic detection and segmentation of brain metastases, in which a neural network is …

Purpose Deep learning-based algorithms have been shown to be able to automatically
detect and segment brain metastases (BMs) in magnetic resonance imaging, mostly based …

Introduction Management of patients with brain metastases is often based on manual lesion
detection and segmentation by an expert reader. This is a time-and labor-intensive process …

Background Approximately one‐fourth of all cancer metastases are found in the brain. MRI
is the primary technique for detection of brain metastasis, planning of radiotherapy, and the …

Objectives To evaluate whether a deep learning (DL) model using both three-dimensional
(3D) black-blood (BB) imaging and 3D gradient echo (GRE) imaging may improve the …

Introduction Deep learning-based algorithms have demonstrated enormous performance in
segmentation of medical images. We collected a dataset of multiparametric MRI and contour …

Background Three-dimensional T1 magnetization prepared rapid acquisition gradient echo
(3D-T1-MPRAGE) is preferred in detecting brain metastases (BM) among MRI. We …