ArticleBMC medical imaging2017
Semi-automated brain tumor segmentation on multi-parametric MRI using regularized non-negative matrix factorization.
Article in BMC medical imaging, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Longitudinal non-negative matrix factorization identifies the altered trajectory of motor symptoms in Parkinson's disease.NPJ Parkinson's disease · 2025Article
- Application of non-negative matrix factorization in oncology: one approach for establishing precision medicine.Briefings in bioinformatics · 2022Review
- Hahn-PCNN-CNN: an end-to-end multi-modal brain medical image fusion framework useful for clinical diagnosis.BMC medical imaging · 2021Article
- Image Features of Magnetic Resonance Imaging under the Deep Learning Algorithm in the Diagnosis and Nursing of Malignant Tumors.Contrast media & molecular imaging · 2021Article
- Deep semi-supervised learning for brain tumor classification.BMC medical imaging · 2020Article
- An Improved Full Convolutional Network Combined with Conditional Random Fields for Brain MR Image Segmentation Algorithm and its 3D Visualization Analysis.Journal of medical systems · 2019Article
- Artificial intelligence in cancer imaging: Clinical challenges and applications.CA: a cancer journal for clinicians · 2019Review
- Embedding MRI information into MRSI data source extraction improves brain tumour delineation in animal models.PloS one · 2019Article
- Machine learning studies on major brain diseases: 5-year trends of 2014-2018.Japanese journal of radiology · 2019Review
- Data Augmentation for Brain-Tumor Segmentation: A Review.Frontiers in computational neuroscience · 2019Review
- Fully automated detection and segmentation of meningiomas using deep learning on routine multiparametric MRI.European radiology · 2019Article
- Automatic brain tissue segmentation based on graph filter.BMC medical imaging · 2018Article
- Rank-Two NMF Clustering for Glioblastoma Characterization.Journal of healthcare engineering · 2018Article
- Magnetic resonance image-based brain tumour segmentation methods: A systematic review.Digital healthReview
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8 authors.
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Abstract
backgroundSegmentation 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.
methodsWe 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.
resultsUsing 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.
conclusionsBased 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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