Comparison of unsupervised classification methods for brain tumor segmentation using multi-parametric MRI

Abstract

Tumor segmentation is a particularly challenging task in high-grade gliomas (HGGs), as they are among the most heterogeneous tumors in oncology. An accurate delineation of the lesion and its main subcomponents contributes to optimal treatment planning, prognosis and follow-up. Conventional MRI (cMRI) is the imaging modality of choice for manual segmentation, and is also considered in the vast majority of automated segmentation studies. Advanced MRI modalities such as perfusion-weighted imaging (PWI), diffusion-weighted imaging (DWI) and magnetic resonance spectroscopic imaging (MRSI) have already shown their added value in tumor tissue characterization, hence there have been recent suggestions of combining different MRI modalities into a multi-parametric MRI (MP-MRI) approach for brain tumor segmentation. In this paper, we compare the performance of several unsupervised classification methods for HGG segmentation based on MP-MRI data including cMRI, DWI, MRSI and PWI. Two independent MP-MRI datasets with a different acquisition protocol were available from different hospitals. We demonstrate that a hierarchical non-negative matrix factorization variant which was previously introduced for MP-MRI tumor segmentation gives the best performance in terms of mean Dice-scores for the pathologic tissue classes on both datasets.

Tumor Tissue Detection using Blood-Oxygen-Level-Dependent Functional MRI based on Independent Component Analysis

“…We first analyze the modality advantage for our method. The conventional-MRI-based tumor segmentation methods are primarily based on the signal intensity difference between the tumor tissue and the surrounding normal tissue 41 , 42 . Some methods jointly utilize T1-, T2- and T2-FLAIR to segment tumors more thoroughly 43 , 44 , while others use only contrast-enhanced T1 to delineate active tumor tissues 45 .…”

Diffusion Kurtosis Imaging Reflects Glial Fibrillary Acidic Protein (GFAP), Topo IIα, and O⁶-Methylguanine-DNA Methyltransferase (MGMT) Expression in Astrocytomas

“…1H-MRS can provide information on the chemical composition of metabolites in living organisms [6]. PWl can assess the perfusion state of tissue microcirculation [7]. DKI, an extension of DTI technology, is a new MRI method for describing the diffusion of non-Gaussian water molecules in tissues.…”

Supervised learning based multimodal MRI brain tumour segmentation using texture features from supervoxels

“…The method was based on graph-cut distribution without involving the training procedure and has low computation time. A comparison of most recent unsupervised methods for brain tumour segmentation was presented in [10]. They also introduced an unsupervised method for segmentation of high grade gliomas (HGG).…”