MRI-based medical image analysis for brain tumor studies is gaining attention in recent
times due to an increased need for efficient and objective evaluation of large amounts of …

Brain imaging is essential to the clinical care of patients with stroke, a leading cause of
disability and death worldwide. Whereas advanced neuroimaging techniques offer …

We propose a dual pathway, 11-layers deep, three-dimensional Convolutional Neural
Network for the challenging task of brain lesion segmentation. The devised architecture is …

Deep learning approaches such as convolutional neural nets have consistently
outperformed previous methods on challenging tasks such as dense, semantic …

Accurate automatic algorithms for the segmentation of brain tumours have the potential of
improving disease diagnosis, treatment planning, as well as enabling large-scale studies of …

Brain tumor segmentation aims to separate the different tumor tissues such as active cells,
necrotic core, and edema from normal brain tissues of White Matter (WM), Gray Matter (GM) …

The potential of improving disease detection and treatment planning comes with accurate
and fully automatic algorithms for brain tumor segmentation. Glioma, a type of brain tumor …

We present a method for automatic segmentation of high-grade gliomas and their
subregions from multi-channel MR images. Besides segmenting the gross tumor, we also …

We present a generative approach for simultaneously registering a probabilistic atlas of a
healthy population to brain magnetic resonance (MR) scans showing glioma and …

We present an approach for segmenting low-and high-grade gliomas in multimodal
magnetic resonance imaging volumes. The proposed approach is based on a hybrid …