Semi-automated brain tumor segmentation on multi-parametric MRI using regularized non-negative matrix factorization

Background Segmentation of gliomas in multi-parametric (MP-)MR images is challenging due to their heterogeneous nature in terms of size, appearance and location. Manual tumor segmentation is a time-consuming task and clinical practice would benefit from (semi-) automated segmentation of the differen...

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Veröffentlicht in:	BMC medical imaging Jg. 17; H. 1; S. 29 - 14
Hauptverfasser:	Sauwen, Nicolas, Acou, Marjan, Sima, Diana M., Veraart, Jelle, Maes, Frederik, Himmelreich, Uwe, Achten, Eric, Huffel, Sabine Van
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London BioMed Central 04.05.2017 Springer Nature B.V BMC
Schlagworte:	Abundance Adult Algorithms Automation Biomarkers Brain Brain cancer Brain Neoplasms - diagnostic imaging Brain Neoplasms - pathology Brain tumors Classification Clinical medicine Compartments Consistency Diffusion effects Factorization Female Gangrene Glioma Glioma - diagnostic imaging Glioma - pathology Growth models Humans Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Image processing Image segmentation Imaging International conferences Machine Learning Magnetic resonance imaging Magnetic Resonance Imaging - methods Male Medicine Medicine & Public Health Metastatic seeding Methods MRI Neural networks Neuroimaging Non-negative matrix factorization Objective function Optimization Patients Pattern Recognition, Automated - methods Perfusion Post-production processing Quality Radiation therapy Radiology Regularization Reproducibility of Results Research Article Robustness Segmentation Sensitivity and Specificity Spatial analysis Spatial distribution State of the art Statistics Temporal variations Training Tumors Unsupervised classification User-Computer Interface Variability Unsupervised classification Segmentation Non-negative matrix factorization MRI Brain tumors
ISSN:	1471-2342, 1471-2342
Online-Zugang:	Volltext
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