Ischemic stroke lesion segmentation using stacked sparse autoencoder

Automatic segmentation of ischemic stroke lesion volumes from multi-spectral Magnetic Resonance Imaging (MRI) sequences plays a vital role in quantifying and locating the lesion region. Most existing methods mainly rely on designing hand-crafted features followed by a classifier model for ischemic s...

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Veröffentlicht in:	Computers in biology and medicine Jg. 99; S. 38 - 52
Hauptverfasser:	Praveen, G.B., Agrawal, Anita, Sundaram, Ponraj, Sardesai, Sanjay
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Elsevier Ltd 01.08.2018 Elsevier Limited
Schlagworte:	Accuracy Automation Brain Classification Classifiers Conflicts of interest Datasets Decomposition Fatalities Image processing Image segmentation Ischemia Ischemic stroke lesion segmentation Lesions Magnetic resonance imaging Medical imaging Methods Neuroimaging NMR Nuclear magnetic resonance Quantitative analysis Recall Sparsity Stacked sparse autoencoders Stroke Support vector machines SVM Unsupervised feature learning Unsupervised feature learning Ischemic stroke lesion segmentation SVM Magnetic resonance imaging Stacked sparse autoencoders
ISSN:	0010-4825, 1879-0534, 1879-0534
Online-Zugang:	Volltext
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