Multiple sclerosis (MS) is a chronic disease. It affects the central nervous system and its
clinical manifestation can variate. Magnetic Resonance Imaging (MRI) is often used to …

Most medical image registration algorithms suffer from a directionality bias that has been
shown to largely impact subsequent analyses. Several approaches have been proposed in …

Glioblastoma extensively infiltrates the brain; despite surgery and aggressive therapies, the
prognosis is poor. A multidisciplinary approach combining mathematical, clinical and …

We present a study of multiple sclerosis segmentation algorithms conducted at the
international MICCAI 2016 challenge. This challenge was operated using a new open …

T1-weighted image (T1WI) and T2-weighted image (T2WI) are the two routinely acquired
magnetic resonance (MR) modalities that can provide complementary information for clinical …

In recent years, several convolutional neural network (CNN) methods have been proposed
for the automated white matter lesion segmentation of multiple sclerosis (MS) patient …

Most image segmentation algorithms are trained on binary masks formulated as a
classification task per pixel. However, in applications such as medical imaging, this “black …

MRI plays a crucial role in multiple sclerosis diagnostic and patient follow-up. In particular,
the delineation of T2-FLAIR hyperintense lesions is crucial although mostly performed …

Recently, segmentation methods based on Convolutional Neural Networks (CNNs) showed
promising performance in automatic Multiple Sclerosis (MS) lesions segmentation. These …

Mood depressive disorder is one of the most disabling chronic diseases with a high rate of
everyday life disability that affects 350 million people around the world. Recent advances in …