Image segmentation is one of the most important tasks in medical image analysis and is
often the first and the most critical step in many clinical applications. In brain MRI analysis …

Every year, millions of brain magnetic resonance imaging (MRI) scans are acquired in
hospitals across the world. These have the potential to revolutionize our understanding of …

Despite advances in data augmentation and transfer learning, convolutional neural
networks (CNNs) difficultly generalise to unseen domains. When segmenting brain scans …

Every year, millions of brain MRI scans are acquired in hospitals, which is a figure
considerably larger than the size of any research dataset. Therefore, the ability to analyze …

In the early days of PET, the partial volume effect (PVE) was identified as a serious factor
affecting image quality and limiting the accuracy of quantitative analysis. Because of the …

Automated analysis of MRI data of the subregions of the hippocampus requires
computational atlases built at a higher resolution than those that are typically used in current …

We introduce a strategy for learning image registration without acquired imaging data,
producing powerful networks agnostic to contrast introduced by magnetic resonance …

We report here on recent developments and advances in pore-scale X-ray tomographic
imaging of subsurface porous media. Our particular focus is on immiscible multi-phase fluid …

We introduce Atropos, an ITK-based multivariate n-class open source segmentation
algorithm distributed with ANTs (http://www. picsl. upenn. edu/ANTs). The Bayesian …

Measures of overlap of labelled regions of images, such as the Dice and Tanimoto
coefficients, have been extensively used to evaluate image registration and segmentation …