Brain tumors are one of the most fatal cancers. Magnetic Resonance Imaging (MRI) is a non-
invasive method that provides multi-modal images containing important information …

Medical practitioners generally rely on multimodal brain images, for example based on the
information from the axial, coronal, and sagittal views, to inform brain tumor diagnosis …

The existing deep convolutional neural networks (DCNNs) based methods have achieved
significant progress regarding automatic glioma segmentation in magnetic resonance …

Most of the current state-of-the-art methods for tumor segmentation are based on machine
learning models trained manually on segmented images. This type of training data is …

Purpose Owing to histologic complexities of brain tumors, its diagnosis requires the use of
multimodalities to obtain valuable structural information so that brain tumor subregions can …

Traditional deep convolutional neural networks for fully automatic brain tumor segmentation
have two problems: spatial information loss caused by both the repeated pooling/striding …

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

Objective Accurately segmenting and quantifying brain gliomas from magnetic resonance
(MR) images remains a challenging task because of the large spatial and structural …

Segmentation of brain lesions from magnetic resonance images (MRI) is an important step
for disease diagnosis, surgical planning, radiotherapy and chemotherapy. However, due to …

Brain tumor segmentation is an important procedure for early tumor diagnosis and
radiotherapy planning. Although numerous brain tumor segmentation methods have been …