This report presents an overview of how machine learning is rapidly advancing clinical
translational imaging in ways that will aid in the early detection, prediction, and treatment of …

Innovations in data visualization punctuate the landmark advances in human connectome
research since its beginnings. From tensor glyphs for diffusion-weighted imaging, to …

Background Analytic morphomics, or more simply,“morphomics,” refers to the measurement
of specific biomarkers of body composition from medical imaging, most commonly computed …

Human neuroscience research faces several challenges with regards to reproducibility.
While scientists are generally aware that data sharing is important, it is not always clear how …

Accurate segmentations in medical images are the foundations for various clinical
applications. Advances in machine learning-based techniques show great potential for …

In this report, we review the current state of the art of web‐based visualization applications.
Recently, an increasing number of web‐based visualization applications have emerged …

Recent years have seen massive, distributed datasets become the norm in neuroimaging
research, and the methodologies used analyze them have, in response, become more …

Proofreading refers to the manual correction of automatic segmentations of image data. In
connectomics, electron microscopy data is acquired at nanometer-scale resolution and …

This paper describes an effort to organize the condensed phases thermodynamic data freely
available in electronic form within the scientific literature through the design of a special …

Connectomics has recently begun to image brain tissue at nanometer resolution, which
produces petabytes of data. This data must be aligned, labeled, proofread, and formed into …