The ENIGMA Stroke Recovery Working Group: Big data neuroimaging to study brain–behavior relationships after stroke

The goal of the Enhancing Neuroimaging Genetics through Meta‐Analysis (ENIGMA) Stroke Recovery working group is to understand brain and behavior relationships using well‐powered meta‐ and mega‐analytic approaches. ENIGMA Stroke Recovery has data from over 2,100 stroke patients collected across 39 research studies and 10 countries around the world, comprising the largest multisite retrospective stroke data collaboration to date. This article outlines the efforts taken by the ENIGMA Stroke Recovery working group to develop neuroinformatics protocols and methods to manage multisite stroke brain magnetic resonance imaging, behavioral and demographics data. Specifically, the processes for scalable data intake and preprocessing, multisite data harmonization, and large‐scale stroke lesion analysis are described, and challenges unique to this type of big data collaboration in stroke research are discussed. Finally, future directions and limitations, as well as recommendations for improved data harmonization through prospective data collection and data management, are provided

An automatic visual analysis system for tennis

This article presents a novel video analysis system for coaching tennis players of all levels, which uses computer vision algorithms to automatically edit and index tennis videos into meaningful annotations. Existing tennis coaching software lacks the ability to automatically index a tennis match into key events, and therefore, a coach who uses existing software is burdened with time-consuming manual video editing. This work aims to explore the effectiveness of a system to automatically detect tennis events. A secondary aim of this work is to explore the bene- fits coaches experience in using an event retrieval system to retrieve the automatically indexed events. It was found that automatic event detection can significantly improve the experience of using video feedback as part of an instructional coaching session. In addition to the automatic detection of key tennis events, player and ball movements are automati- cally tracked throughout an entire match and this wealth of data allows users to find interesting patterns in play. Player and ball movement information are integrated with the automatically detected tennis events, and coaches can query the data to retrieve relevant key points during a match or analyse player patterns that need attention. This coaching software system allows coaches to build advanced queries, which cannot be facilitated with existing video coaching solutions, without tedious manual indexing. This article proves that the event detection algorithms in this work can detect the main events in tennis with an average precision and recall of 0.84 and 0.86, respectively, and can typically eliminate man- ual indexing of key tennis events

Application of machine learning to automated analysis of cerebral edema in large cohorts of ischemic stroke patients

Cerebral edema contributes to neurological deterioration and death after hemispheric stroke but there remains no effective means of preventing or accurately predicting its occurrence. Big data approaches may provide insights into the biologic variability and genetic contributions to severity and time course of cerebral edema. These methods require quantitative analyses of edema severity across large cohorts of stroke patients. We have proposed that changes in cerebrospinal fluid (CSF) volume over time may represent a sensitive and dynamic marker of edema progression that can be measured from routinely available CT scans. To facilitate and scale up such approaches we have created a machine learning algorithm capable of segmenting and measuring CSF volume from serial CT scans of stroke patients. We now present results of our preliminary processing pipeline that was able to efficiently extract CSF volumetrics from an initial cohort of 155 subjects enrolled in a prospective longitudinal stroke study. We demonstrate a high degree of reproducibility in total cranial volume registration between scans (R = 0.982) as well as a strong correlation of baseline CSF volume and patient age (as a surrogate of brain atrophy, R = 0.725). Reduction in CSF volume from baseline to final CT was correlated with infarct volume (R = 0.715) and degree of midline shift (quadratic model, p < 2.2 × 10−16). We utilized generalized estimating equations (GEE) to model CSF volumes over time (using linear and quadratic terms), adjusting for age. This model demonstrated that CSF volume decreases over time (p < 2.2 × 10−13) and is lower in those with cerebral edema (p = 0.0004). We are now fully automating this pipeline to allow rapid analysis of even larger cohorts of stroke patients from multiple sites using an XNAT (eXtensible Neuroimaging Archive Toolkit) platform. Data on kinetics of edema across thousands of patients will facilitate precision approaches to prediction of malignant edema as well as modeling of variability and further understanding of genetic variants that influence edema severity

Imaging in population science: cardiovascular magnetic resonance in 100,000 participants of UK Biobank - rationale, challenges and approaches

PMCID: PMC3668194SEP was directly funded by the National Institute for Health Research Cardiovascular Biomedical Research Unit at Barts. SN acknowledges support from the Oxford NIHR Biomedical Research Centre and from the Oxford British Heart Foundation Centre of Research Excellence. SP and PL are funded by a BHF Senior Clinical Research fellowship. RC is supported by a BHF Research Chair and acknowledges the support of the Oxford BHF Centre for Research Excellence and the MRC and Wellcome Trust. PMM gratefully acknowledges training fellowships supporting his laboratory from the Wellcome Trust, GlaxoSmithKline and the Medical Research Council

Towards Structured Analysis of Broadcast Badminton Videos

Sports video data is recorded for nearly every major tournament but remains archived and inaccessible to large scale data mining and analytics. It can only be viewed sequentially or manually tagged with higher-level labels which is time consuming and prone to errors. In this work, we propose an end-to-end framework for automatic attributes tagging and analysis of sport videos. We use commonly available broadcast videos of matches and, unlike previous approaches, does not rely on special camera setups or additional sensors. Our focus is on Badminton as the sport of interest. We propose a method to analyze a large corpus of badminton broadcast videos by segmenting the points played, tracking and recognizing the players in each point and annotating their respective badminton strokes. We evaluate the performance on 10 Olympic matches with 20 players and achieved 95.44% point segmentation accuracy, 97.38% player detection score ([email protected]), 97.98% player identification accuracy, and stroke segmentation edit scores of 80.48%. We further show that the automatically annotated videos alone could enable the gameplay analysis and inference by computing understandable metrics such as player's reaction time, speed, and footwork around the court, etc.Comment: 9 page