Multiple Sclerosis

Improving the detection of new lesions in multiple sclerosis with a cascaded 3D fully convolutional neural network approach

Frontiers in Neuroscience, 2022. Quality index: (JCR N IF 3.707, Q2(97/272)).

Evaluating the use of synthetic T1-w images in new T2 lesion detection in multiple sclerosis

Frontiers in Neuroscience, Brain Imaging Methods, 2022. Quality index: (JCR N IF 5.152, Q2(87/274)).

Deep learning methods for automated detection of new multiple sclerosis lesions in longitudinal magnetic resonance images

PhD thesis in Brain Medical Image Analysis, Department of Computer Architecture and Technology, University of Girona, Spain, 2020.

A fully convolutional neural network for new T2-w lesion detection in multiple sclerosis

NeuroImage: Clinical, 2020. Quality index: (JCR N IF 3.943, Q1(3/14)).

Multiple Sclerosis Lesion Synthesis in MRI Using an Encoder-Decoder U-NET

IEEE Access, 2019. Quality index: (JCR CSIS IF 4.098, Q1(23/155)).

Detecting the appearance of new T2-w multiple sclerosis lesions in longitudinal studies using deep convolutional neural networks

Abstract in Multiple Sclerosis Journal, Stockholm Sweden, September, 2019 (JCR CN IF:5.649 Q1(23/199)).

Lesion synthesis for extending MRI training datasets and improving automatic multiple sclerosis lesion segmentation

Abstract in Multiple Sclerosis Journal, Stockholm Sweden, September, 2019 (JCR CN IF:5.649 Q1(23/199)).

One-shot domain adaptation in multiple sclerosis lesion segmentation using convolutional neural networks

NeuroImage: Clinical, 2019. Quality index: (JCR N IF 3.943, Q1(3/14)).

Manual delineation of only one image in unseen databases is sufficient for accurate performance in automated multiple sclerosis lesion segmentation

Abstract in Multiple Sclerosis Journal, Berlin, Germany, October, 2018 (JCR CN IF:5.649 Q1(23/199)).

A supervised framework with intensity subtraction and deformation field features for the detection of new T2-w lesions in multiple sclerosis

NeuroImage: Clinical, 2018. Quality index: (JCR N IF 3.943, Q1(3/14)).