Imputation of missing images via source-to-target modality translation can improve diversity
in medical imaging protocols. A pervasive approach for synthesizing target images involves …

Generative adversarial models with convolutional neural network (CNN) backbones have
recently been established as state-of-the-art in numerous medical image synthesis tasks …

Medical images have been widely used in clinics, providing visual representations of under-
skin tissues in human body. By applying different imaging protocols, diverse modalities of …

Medical imaging plays a critical role in various clinical applications. However, due to
multiple considerations such as cost and radiation dose, the acquisition of certain image …

We propose a dual pathway, 11-layers deep, three-dimensional Convolutional Neural
Network for the challenging task of brain lesion segmentation. The devised architecture is …

Acquiring images of the same anatomy with multiple different contrasts increases the
diversity of diagnostic information available in an MR exam. Yet, the scan time limitations …

Magnetic resonance (MR) imaging is a widely used medical imaging protocol that can be
configured to provide different contrasts between the tissues in human body. By setting …

A key limitation of deep convolutional neural network (DCNN)-based image segmentation
methods is the lack of generalizability. Manually traced training images are typically required …

We propose a multi-input multi-output fully convolutional neural network model for MRI
synthesis. The model is robust to missing data, as it benefits from, but does not require …

Magnetic resonance imaging (MRI) is being increasingly utilized to assess, diagnose, and
plan treatment for a variety of diseases. The ability to visualize tissue in varied contrasts in …