15 citations · 16 across the 4 of their papers we have counts for
5 papers · 1 filter
Multiple Sclerosis Lesion Synthesis in MRI using an encoder-decoder U-NET
Mostafa Salem, Sergi Valverde, Mariano Cabezas +5
In this paper, we propose generating synthetic multiple sclerosis (MS) lesions on MRI images with the final aim to improve the performance of supervised machine learning algorithms…
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
Spyridon Bakas, Mauricio Reyes, Andras Jakab +421
Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritum…
One-shot domain adaptation in multiple sclerosis lesion segmentation using convolutional neural networks
Sergi Valverde, Mostafa Salem, Mariano Cabezas +7
In recent years, several convolutional neural network (CNN) methods have been proposed for the automated white matter lesion segmentation of multiple sclerosis (MS) patient images,…
Quantitative analysis of patch-based fully convolutional neural networks for tissue segmentation on brain magnetic resonance imaging
Jose Bernal, Kaisar Kushibar, Mariano Cabezas +3
Accurate brain tissue segmentation in Magnetic Resonance Imaging (MRI) has attracted the attention of medical doctors and researchers since variations in tissue volume help in diag…
Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approach
Sergi Valverde, Mariano Cabezas, Eloy Roura +7
In this paper, we present a novel automated method for White Matter (WM) lesion segmentation of Multiple Sclerosis (MS) patient images. Our approach is based on a cascade of two 3D…