Accurate segmentation of infant brain magnetic resonance (MR) images into white matter (WM),
gray matter (GM), and cerebrospinal fluid is an indispensable foundation for early …

Gliomas are the most common primary brain malignancies, with different degrees of
aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, ie, …

In this work we approach the brain tumor segmentation problem with a cascade of two CNNs
inspired in the V-Net architecture [ 13 ], reformulating residual connections and making use …

… Saeed1,2, Adrià Casamitjana2, Zachary MC Baum1,2, Rémi Delaunay1,4, Qianye Yang1,2,
Alexander Grimwood1,2, Zhe Min1, Stefano B. Blumberg2, Juan Eugenio Iglesias2,5,6 …

Quantification of cerebral white matter hyperintensities (WMH) of presumed vascular origin
is of key importance in many neurological research studies. Currently, measurements are …

Ex vivo imaging enables analysis of the human brain at a level of detail that is not possible
in vivo with MRI. In particular, histology can be used to study brain tissue at the microscopic …

Background Magnetic resonance imaging (MRI) has unveiled specific alterations at different
stages of Alzheimer’s disease (AD) pathophysiologic continuum constituting what has been …

This paper analyzes the use of 3D Convolutional Neural Networks for brain tumor segmentation
in MR images. We address the problem using three different architectures that combine …

Nonlinear inter-modality registration is often challenging due to the lack of objective functions
that are good proxies for alignment. Here we propose a synthesis-by-registration method …

The identification of healthy individuals harboring amyloid pathology represents one
important challenge for secondary prevention clinical trials in Alzheimer’s disease (AD). …