Volume Fusions

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Overcome limitations and fuse volumes

Some samples may be too large to be imaged in a single field of view at the desired resolution. Mosaic tiling can commonly workaround this limitation and the arivis Tile Sorter can conveniently stitch such images into a single large volume. In some cases mosaic tiling may not be sufficient and it may be necessary to move/turn the sample to image the complete volume which can prevent stitching methods that rely on translation alone. The new arivis Volume Fusion Module overcomes these limitations and allows to fuse volumes based on landmarks and/or the free movement, rotation and scaling of a surface representation.

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Image Courtesy of vDisco technology, Ertürk Lab, Institute for Stroke and Dementia Research, Klinikum der Universität München, Munich, Germany

Benefits & Applications

  • Fusing a number of large data sets into one huge data set
  • Easy analysis of the complete sample in one data set
  • Measurement of long structures (neurons and vessels) using VisionVR, even in large tissues which are usually too big for a single microscope field of view
  • Merge volumes as separate channels for multimodal imaging
  • Create a single large 3D data set from multiple sub volumes
  • Work around light penetration issues in light sheet microscopy by combining different views
  • Combine different imaging modalities or resolutions of the same sample

The arivis Imaging Platform

The arivis Imaging Platform is a flexible computing universe for Imaging Science that scales, parallelizes, integrates and connects all imaging workflows, sparking organization-wide image data proficiency and efficiency at all levels. The integrated toolsets take care of everything from the file storage format to user and project-specific computations to reporting. The computational and management hubs that comprise the platform connect your datasets and take care of your central imaging databases and can expose data assets - including raw data and specified portions of raw data - to Machine Learning and AI routines.


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