In biomedical image analysis, information about the location and appearance of tumors and
lesions is indispensable to aid doctors in treating and identifying the severity of diseases …

Objectives Accurate and reliable segmentation of brain tumors from MRI images helps in
planning an enhanced treatment and increases the life expectancy of patients. However, the …

Abstract Machine learning analysis of neuroimaging data can accurately predict
chronological age in healthy people. Deviations from healthy brain ageing have been …

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) …

Accurate segmentation of brain magnetic resonance imaging (MRI) is an essential step in
quantifying the changes in brain structure. Deep learning in recent years has been …

MRI brain image analysis, including brain tumor detection, is a challenging task. MRI images
are multimodal, and in recent years, multimodal medical image analysis has gotten more …

Classical diffeomorphic image registration methods, while being accurate, face the
challenges of high computational costs. Deep learning based approaches provide a fast …

Inference of tumor and edema areas from brain magnetic resonance imaging (MRI) data
remains challenging owing to the complex structure of brain tumors, blurred boundaries, and …

This study created and tested a database of adult, age-specific MRI brain and head
templates. The participants included healthy adults from 20 through 89 years of age. The …

The segmentation of brain tissue in MRI is valuable for extracting brain structure to aid
diagnosis, treatment and tracking the progression of different neurologic diseases. Medical …