Multiple Sclerosis (MS) is a type of brain disease which causes visual, sensory, and motor
problems for people with a detrimental effect on the functioning of the nervous system. In …

In recent years, deep convolutional neural networks (CNNs) have shown record-shattering
performance in a variety of computer vision problems, such as visual object recognition …

Generative adversarial networks (GANs) have become increasingly powerful, generating
mind-blowing photorealistic images that mimic the content of datasets they have been …

Deep learning networks have recently been shown to outperform other segmentation
methods on various public, medical-image challenge datasets, particularly on metrics …

In this paper, we study the influence of both long and short skip connections on Fully
Convolutional Networks (FCN) for biomedical image segmentation. In standard FCNs, only …

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

Colorectal cancer (CRC) is the third cause of cancer death worldwide. Currently, the
standard approach to reduce CRC‐related mortality is to perform regular screening in …

In this paper we report the set-up and results of the Multimodal Brain Tumor Image
Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and …

We propose a novel segmentation approach based on deep 3D convolutional encoder
networks with shortcut connections and apply it to the segmentation of multiple sclerosis …

The Sørensen-Dice index (SDI) is a widely used measure for evaluating medical image
segmentation algorithms. It offers a standardized measure of segmentation accuracy which …