Scale Up Your Image Analysis
ZEISS arivis Hub for accelerated analysis and faster results.
ZEISS arivis Hub enables researchers in diverse scientific and industrial applications to streamline image analysis on a large scale providing faster results. Whether datasets are already stored or being actively acquired, ZEISS arivis Hub handles images from a diverse range of imaging systems with ease.
A centralized system, either on local servers or in the cloud, ZEISS arivis Hub allows users to efficiently organize and share their imaging data. With multiple analysis resources deployed in parallel, users can simultaneously process an array of images with various pipelines, thereby reducing overall completion times for projects.
ZEISS arivis Hub for HCA
ZEISS HCA solutions are designed to provide you with the tools and capabilities you need to drive groundbreaking discoveries, accelerate drug development, and unlock new frontiers in scientific exploration.
Accelerate your biotech and pharmaceutical research with advanced high content analysis (HCA) software solutions from ZEISS. This powerful suite of software tools enables you to streamline your workflows, uncover valuable insights, and drive innovation across a wide range of applications, whether you are dealing with 2D data or handling large multi-dimensional datasets.
Overcome the challenge of large-scale image analysis
Analyzing thousands of images to uncover crucial details can be a daunting task amidst vast datasets. The challenge is not to miss that one detail you need. With an ever-increasing amount of imaging data being produced from a range of systems at core imaging facilities to CRO’s and pharma, it is becoming ever more critical to consolidate imaging data.
Break free of the image analysis bottleneck
Avoid data redundancy with data silos and disparate analysis workflows. ZEISS arivis Hub allows for a centralized system offering streamlined data ingestion and analysis for wide-ranging file types.
With optimized resource allocation, you maximize computing potential while minimizing inefficiencies like wasted space, energy consumption, and control costs.
Learn more here.
