Service-oriented visualization applied to medical data analysis

With the era of Grid computing, data driven experiments and simulations have become very advanced and complicated. To allow specialists from various domains to deal with large datasets, aside from developing efficient extraction techniques, it is necessary to have available computational facilities to visualize and interact with the results of an extraction process. Having this in mind, we developed an Interactive Visualization Framework, which supports a service-oriented architecture. This framework allows, on one hand visualization experts to construct visualizations to view and interact with large datasets, and on the other hand end-users (e.g., medical specialists) to explore these visualizations irrespective of their geographical location and available computing resources. The image-based analysis of vascular disorders served as a case study for this project. The paper presents main research findings and reports on the current implementation status

The Resource Identification Initiative: A cultural shift in publishing

A central tenet in support of research reproducibility is the ability to uniquely identify research resources, i.e., reagents, tools, and materials that are used to perform experiments. However, current reporting practices for research resources are insufficient to allow humans and algorithms to identify the exact resources that are reported or answer basic questions such as What other studies used resource X? To address this issue, the Resource Identification Initiative was launched as a pilot project to improve the reporting standards for research resources in the methods sections of papers and thereby improve identifiability and reproducibility. The pilot engaged over 25 biomedical journal editors from most major publishers, as well as scientists and funding officials. Authors were asked to include Research Resource Identifiers (RRIDs) in their manuscripts prior to publication for three resource types: antibodies, model organisms, and tools (including software and databases). RRIDs represent accession numbers assigned by an authoritative database, e.g., the model organism databases, for each type of resource. To make it easier for authors to obtain RRIDs, resources were aggregated from the appropriate databases and their RRIDs made available in a central web portal ( www.scicrunch.org/resources). RRIDs meet three key criteria: they are machine readable, free to generate and access, and are consistent across publishers and journals. The pilot was launched in February of 2014 and over 300 papers have appeared that report RRIDs. The number of journals participating has expanded from the original 25 to more than 40. Here, we present an overview of the pilot project and its outcomes to date. We show that authors are generally accurate in performing the task of identifying resources and supportive of the goals of the project. We also show that identifiability of the resources pre- and post-pilot showed a dramatic improvement for all three resource types, suggesting that the project has had a significant impact on reproducibility relating to research resources

3D reconstruction data used to perform selection task

In the selection task, participants had to select the region of interest (a specified vessel segment) as explained in the "Experiment_description.pdf" file. They were asked to manipulate (i.e., translate, rotate and scale) a 3D box widget, initially positioned such that the region of interest covered all vessel structures displayed on the screen. Three 3D model files ("level1.vtk", "level2.vtk" and "level.3 vtk") were used to define three complexity levels of the selection task: Level 1 (simple) — one vessel; Level 2 (average) — two closely located vessels; Level 3 (complex) — three vessels, where two vessels are located close to each other. This project was funded by the University of Amsterdam and NWO

3D reconstruction data used to perform selection task

In the selection task, participants had to select the region of interest (a specified vessel segment) as explained in the "Experiment_description.pdf" file. They were asked to manipulate (i.e., translate, rotate and scale) a 3D box widget, initially positioned such that the region of interest covered all vessel structures displayed on the screen. Three 3D model files ("level1.vtk", "level2.vtk" and "level.3 vtk") were used to define three complexity levels of the selection task: Level 1 (simple) — one vessel; Level 2 (average) — two closely located vessels; Level 3 (complex) — three vessels, where two vessels are located close to each other. This project was funded by the University of Amsterdam and NWO. This is a new version of the dataset to test RDM implementation

3D reconstruction data used to perform selection task

In the selection task, participants had to select the region of interest (a specified vessel segment) as explained in the "Experiment_description.pdf" file. They were asked to manipulate (i.e., translate, rotate and scale) a 3D box widget, initially positioned such that the region of interest covered all vessel structures displayed on the screen. Three 3D model files ("level1.vtk", "level2.vtk" and "level.3 vtk") were used to define three complexity levels of the selection task: Level 1 (simple) — one vessel; Level 2 (average) — two closely located vessels; Level 3 (complex) — three vessels, where two vessels are located close to each other. This project was funded by the University of Amsterdam and NWO. This is version 8 of the dataset to test RDM implementation

Results of the 3D postioning user study

The higher complexity levels of the positioning task require much more time and produce many more errors irrespective of the input method applied. ANOVA found that the positioning task was performed significantly faster with the 3D glove (M=139.1 s, SD=12) than with the 2D glove (M=198.4 s, SD=21.5), F1, 29=11.4, p=0.002. As can be seen in Fig. 9-left, the decrease in the task completion time was much higher for Levels 2 and 3 than for Level 1. The discovered significant differences between 2D and 3D input were 88.3 s (p=0.001) for the most complex trial (Level 3) and 71.8 s (p<0.001) for the slightly less complex trial (Level 2). (this is version 5 to test RDM implementations

Results of the 3D postioning user study

The higher complexity levels of the positioning task require much more time and produce many more errors irrespective of the input method applied.ANOVA found that the positioning task was performed significantly faster with the 3D glove (M=139.1 s, SD=12) than with the 2D glove (M=198.4 s, SD=21.5), F1, 29=11.4, p=0.002. As can be seen in Fig. 9-left, the decrease in the task completion time was much higher for Levels 2 and 3 than for Level 1. The discovered significant differences between 2D and 3D input were 88.3 s (p=0.001) for the most complex trial (Level 3) and 71.8 s (p&lt;0.001) for the slightly less complex trial (Level 2).(this is version 6 to test RDM implementations

Results of the 3D postioning user study

The higher complexity levels of the positioning task require much more time and produce many more errors irrespective of the input method applied.ANOVA found that the positioning task was performed significantly faster with the 3D glove (M=139.1 s, SD=12) than with the 2D glove (M=198.4 s, SD=21.5), F1, 29=11.4, p=0.002. As can be seen in Fig. 9-left, the decrease in the task completion time was much higher for Levels 2 and 3 than for Level 1. The discovered significant differences between 2D and 3D input were 88.3 s (p=0.001) for the most complex trial (Level 3) and 71.8 s (p&lt;0.001) for the slightly less complex trial (Level 2).(this is version 7 to test embargo