Graz User Story

PROJECT

Use Nodeum to reduce metadata analysis time by 10%, streamlining the data movement process and enhancing efficiency. Say goodbye to manual tasks and hello to seamless metadata analysis.

WHAT WE DID

We streamline the entire data analysis process, automating the data movement from slide scanners to the analysis system and ultimately to archival storage. This significantly speeds up the overall process by a factor of 10.

CLIENT

Medical University of Graz, (www.medunigraz.at)

TIMELINE

November 2020

PROJECT INFORMATION

The AI research team around Dr. Heimo Müller, in close has created the infrastructural basis for digitalization in pathology, which actively supports Data Enrichment and Data Transfer to ensure Data Storage and long-term archiving.

The aim is to provide value add on those generated slides from high-performance scanners. The research team want to ensure the quality of data, to enrich it with additional information (tags) and to carry out and optimize the data transfer in an object-oriented manner.

The objective of the project is to continue to build a central archive with digitalized pathological data. In the future, the team will be able to analyze large historical databases with AI mechanisms and make comparisons with National and International Biomedical database and research resource, containing in-depth genetic and health information.

The information obtained in this way helps the pathologist in diagnostics, and pharmaceutical research can conduct goal-oriented research and develop drugs and treatment methods “right to the point”.

3 BIG CHALLENGES TO SOLVE

The Medical University of Graz want to create the first technical and organizational foundations for digital pathology and optimize data storage, data enrichment and data transfer through the use of high-performance GPU-based servers for the use of special pathological AI algorithms, all-flash storage systems and the multifunctional data mover.

Ensure the quality of the data generated by high-performance scanners.
Enrich it with additional information (tags) and to carry out.
Optimize the data transfer in an object-oriented manner.

Accelerate by 10 the time to process the data analysis

RESULTS

The AI research team around Dr. Heimo Müller, in close, long-term cooperation with AKQUINET and NODEUM has created the infrastructural basis for digitalization in pathology, which actively supports Data Enrichment and Data Transfer to ensure Data Storage and long-term archiving.

“ In addition to the data management optimization between our heterogeneous system landscapes, we consider important to promote collaboration within European research partners. In using the convenient and technically mature data mover NODEUM, we expect efficiency and data security thanks to the links between our data sources, but also via APIs, taking into account all role and authorization models. ”

Docteur Heimo MüllerLarge Scale Slide Digitalization for ML Computational Pathology - Research Group Leader

"The collaboration in the project is very cooperative. Each partner has been involved in every phase of the project right from the start. In this way, the project duration is constantly optimized and friction losses are kept as low as possible. Now that the complex tests of the hardware and software functionalities have been completed, we are starting production and the individual adaptation/ extension of the application, especially for new research projects."

Robert ReihsNodeum project manager, Medical University of Graz

Medical University Of Graz

Time to analyze metadata are reduced by 10 in using Nodeum

PROJECT INFORMATION

Collection of Carcomina images processed by scanners

Prior Nodeum implementation

3 BIG CHALLENGES TO SOLVE

RESULTS

Nodeum cuts data analysis time by a factor of 10

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