Author:

Keywords:

Non-negative matrix factorization, MRI, unsupervised learning, brain tumors, segmentation

Abstract:

Gliomas represent about 80% of all malignant primary brain tumors. Despite recent advancements in glioma research, patient outcome remains poor. Magnetic resonance imaging (MRI) has become the imaging modality of choice in the management of brain tumor patients. Over the past decade, advanced MRI modalities, such as perfusion-weighted imaging, diffusion-weighted imaging and magnetic resonance spectroscopic imaging have gained interest in the clinical field, and their added value has been recognized. Tumor segmentation plays an important role in treatment planning as well as during follow-up. Manual segmentation by a clinical expert is currently the gold standard, but it is a tedious and time-consuming task. Clinical practice would benefit from accurate and automated volumetric delineation of the tumor and its sub-compartments. Throughout this PhD, methods have been developed for automated segmentation and characterization of brain tumors. The proposed methods are based on an unsupervised learning technique called non-negative matrix factorization (NMF). NMF provides an additive parts-based representation of the input data, revealing the basic components which are present. Applied to the multi-parametric MR imaging data of a brain tumor patient, NMF is able to extract tissue-specific signatures as well as the relative proportions of the different tissue types in each voxel. Being an unsupervised method, NMF cannot benefit from an extensive training dataset to learn decision boundaries between tissue classes, but it is directly applicable to any multi-parametric MRI dataset of any individual patient.