Automatic brain structure segmentation in Magnetic Resonance Image (MRI) plays an
important role in the diagnosis of various neuropsychiatric diseases. However, most existing …

In medical applications, the same anatomical structures may be observed in multiple
modalities despite the different image characteristics. Currently, most deep models for …

Accuracy segmentation of brain structures could be helpful for glioma and radiotherapy
planning. However, due to the visual and anatomical differences between different …

The morphology, symmetry, and volume of brain tissue are good indicators for measuring
the central nervous system disease progression. The objective of this paper is to segment …

Segmenting deep brain structures from magnetic resonance images is important for patient
diagnosis, surgical planning, and research. Most current state‐of‐the‐art solutions follow a …

Brain tumor segmentation is an important direction in medical image processing, and its
main goal is to accurately mark the tumor part in brain MRI. This study proposes a brand …

Brain tumor segmentation from Magnetic Resonance Imaging is essential for early diagnosis
and treatment planning for brain cancers in clinical practice. However, existing brain tumor …

Background Cerebrovascular segmentation is a crucial step in the computer‐assisted
diagnosis of cerebrovascular pathologies. However, accurate extraction of cerebral vessels …

There have been few anatomical structure segmentation studies using deep learning.
Numbers of training and ground truth images applied were small and the accuracies of …

Automated brain segmentation is an active research domain due to the association of
various neurological disorders with different regions of the brain, to help medical …