Since the second half of the twentieth century, intracranial electroencephalography (iEEG),
including both electrocorticography (ECoG) and stereo-electroencephalography (sEEG) …

The damaged areas of brain tissues can be extracted by using segmentation methods, most
of which are based on the integration of machine learning and data mining techniques. An …

MRI-based tractography is still underexploited and unsuited for routine use in brain tumor
surgery due to heterogeneity of methods and functional–anatomical definitions and above …

Clinical decision support systems (CDSSs) have gained critical importance in clinical
practice and research. Machine learning (ML) and deep learning methods are widely …

Objectives: Automated whole brain segmentation from magnetic resonance images is of
great interest for the development of clinically relevant volumetric markers for various …

Objective To investigate the relationship between neurocognitive deficits and structural
changes on brain magnetic resonance imaging in people living with HIV (PLWH) with good …

Introduction Automatic whole brain and lesion segmentation at 7T presents challenges,
primarily from bias fields, susceptibility artifacts including distortions, and registration errors …

Accurately extracting brain tissue is a critical and primary step in brain neuroimaging
research. Due to the differences in brain size and structure between humans and nonhuman …

Abstract Objective Focal cortical dysplasias (FCDs) are a common cause of apparently non-
lesional drug-resistant focal epilepsy. Visual detection of subtle FCDs on MRI is clinically …

HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM/TSP) is a chronic,
progressive neurological disorder and shares many radiological and clinical features with …