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BMC Medical Imaging
Published in: BMC Medical Imaging 1/2017

Open Access 01-12-2017 | Research Article

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

Authors: Nicolas Sauwen, Marjan Acou, Diana M. Sima, Jelle Veraart, Frederik Maes, Uwe Himmelreich, Eric Achten, Sabine Van Huffel

Published in: BMC Medical Imaging | Issue 1/2017

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Abstract

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 different tumor compartments.

Methods

We present a semi-automated framework for brain tumor segmentation based on non-negative matrix factorization (NMF) that does not require prior training of the method. L1-regularization is incorporated into the NMF objective function to promote spatial consistency and sparseness of the tissue abundance maps. The pathological sources are initialized through user-defined voxel selection. Knowledge about the spatial location of the selected voxels is combined with tissue adjacency constraints in a post-processing step to enhance segmentation quality. The method is applied to an MP-MRI dataset of 21 high-grade glioma patients, including conventional, perfusion-weighted and diffusion-weighted MRI. To assess the effect of using MP-MRI data and the L1-regularization term, analyses are also run using only conventional MRI and without L1-regularization. Robustness against user input variability is verified by considering the statistical distribution of the segmentation results when repeatedly analyzing each patient’s dataset with a different set of random seeding points.

Results

Using L1-regularized semi-automated NMF segmentation, mean Dice-scores of 65%, 74 and 80% are found for active tumor, the tumor core and the whole tumor region. Mean Hausdorff distances of 6.1 mm, 7.4 mm and 8.2 mm are found for active tumor, the tumor core and the whole tumor region. Lower Dice-scores and higher Hausdorff distances are found without L1-regularization and when only considering conventional MRI data.

Conclusions

Based on the mean Dice-scores and Hausdorff distances, segmentation results are competitive with state-of-the-art in literature. Robust results were found for most patients, although careful voxel selection is mandatory to avoid sub-optimal segmentation.
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Metadata
Title
Semi-automated brain tumor segmentation on multi-parametric MRI using regularized non-negative matrix factorization
Authors
Nicolas Sauwen
Marjan Acou
Diana M. Sima
Jelle Veraart
Frederik Maes
Uwe Himmelreich
Eric Achten
Sabine Van Huffel
Publication date
01-12-2017
Publisher
BioMed Central
Published in
BMC Medical Imaging / Issue 1/2017
Electronic ISSN: 1471-2342
DOI
https://doi.org/10.1186/s12880-017-0198-4

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