Conventional and Deep Learning Methods for Skull Stripping in Brain MRI
نویسندگان
چکیده
منابع مشابه
Unsupervised Skull Stripping in MRI
Whole brain segmentation, referred to as skull stripping, is an important technique in neuroimaging. Many applications, such as presurgical planning, cortical surface reconstruction and brain morphometry, depend on the ability to accurately segment brain from non-brain tissue, i.e. remove extra-cerebral tissue such as skull, sclera, orbital fat, skin, etc. However, despite the clear definition ...
متن کاملDeep MRI brain extraction: A 3D convolutional neural network for skull stripping
Brain extraction from magnetic resonance imaging (MRI) is crucial for many neuroimaging workflows. Current methods demonstrate good results on non-enhanced T1-weighted images, but struggle when confronted with other modalities and pathologically altered tissue. In this paper we present a 3D convolutional deep learning architecture to address these shortcomings. In contrast to existing methods, ...
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Skull-stripping separates the skull region of the head from the soft brain tissues. In many cases of brain image analysis, this is an essential preprocessing step in order to improve the final result. This is true for both registration and segmentation tasks. In fact, skull-stripping of magnetic resonance images (MRI) is a well-studied problem with numerous publications in recent years. Many di...
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This paper describes a novel automatic statistical morphology skull stripper (SMSS) that uniquely exploits a statistical self-similarity measure and a 2-D brain mask to delineate the brain. The result of applying SMSS to 20 MRI data set volumes, including scans of both adult and infant subjects is also described. Quantitative performance assessment was undertaken with the use of brain masks pro...
متن کاملA hybrid approach to the skull stripping problem in MRI.
We present a novel skull-stripping algorithm based on a hybrid approach that combines watershed algorithms and deformable surface models. Our method takes advantage of the robustness of the former as well as the surface information available to the latter. The algorithm first localizes a single white matter voxel in a T1-weighted MRI image, and uses it to create a global minimum in the white ma...
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ژورنال
عنوان ژورنال: Applied Sciences
سال: 2020
ISSN: 2076-3417
DOI: 10.3390/app10051773