arivis VisionVR - Analysis in Virtual Reality

Analysis for Life Science research images in VR

arivis VisionVR sets new standards for analyzing Life Science research images. Freed from being tethered to a mouse and with depth perception equivalent to the real world, a person’s hands are unencumbered to simply reach into the data to precisely and intuitively mark, measure, classify, edit and segment. The cumbersome task of viewing 3D data on a desktop involves, multiple turns of the dataset, changing tools multiple times, guessing at which object is being selected, is now reduced to simply reaching out and pulling a trigger or pushing a button. 


Notable arivis VisionVR Analysis Capabilities:

Import, edit and proofread automatically segmented data while comparing against the raw image data
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De-novo segment data automatically, semi-automatically, as well as manually
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Export segments & statistics (position, intensity, size, classification) for further analysis
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Custom label & count structures of interest
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Perform distance & angle measurements
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Interact seamlessly with other platform products
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Segment Proof-Editing

Images - especially fluorescence images -  can be difficult to accurately segment because of a host of reasons:

  • poor signal to noise ratio
  • staining that does not fill objects completely or uniformly
  • the varied intensity of signal between the “same” objects
  • objects that appear to touch one another and really need to be separated because of packing and/or lack of spatial resolution

arivis VisionVR goes beyond simple proofreading to identify areas where automatic segmentation on a desktop program has gone wrong. Preserving the correct portions of the original segmentation, arivis VisionVR provides sculpting and painting tools to interactively grow or shrink segmented objects transforming them to a 100% fit to the original image data. Joining, splitting and deleting objects where automatic segmentation has gone wrong is equally as intuitive. arivis VisionVR significantly increases the efficiency of the proof-editing process. 

De-novo Segmentation

Productivity and accuracy gains provided by arivis VisionVR are not just limited to pre-segmented data. De-novo segmentation can be performed semi-automatically by pointing to a local ROI and pulling the trigger to run a choice of automatic algorithms, thus rapidly segmenting objects literally at your fingertips. When all else fails, segmentation can be performed by sculpting a generic object to fit the original data or by manually painting from scratch.

Distance and Angle Measurements

Performing 3D distance measurements on a desktop is a cumbersome process that involves a lot of guesswork. In VR, this task is a breeze. Measurement Points can be interactively dropped at any location in VR space. The user can choose from point to point, multi-point, and angle measurements. The position of Measurement Points can be interactively modified making the whole process very intuitive. Users can control the size, visibility and if measurement points are subjected to clipping or not. Measurements are recorded for viewing in VR or export to other programs and are calibrated based upon the original voxel (pixel) sizes. 

Count & Classify

When image segmentation is not necessary or simply not possible, manual workflows are needed to analyze images. For counting and classifying objects, arivis VisionVR provides an easy, manual workflow that speeds up simple tasks enormously: Simply point at an object to count and press a button. The structure will be labeled with a clearly visible marker, making double counts impossible. Markers can be named and classified as well, making a very common task to count and categorize objects very simple in 3D.


Export of Segments and Statistics


InViewR 3.1 Data Management Menu

Because segmented data is overlaid on the original volume data in arivis VisionVR, statistical properties of segments can be calculated in real-time based on the original data even as segments are modified, added, and deleted. Statistics on position, size, shape, and per color channel intensity values are calculated. Those statistics can be viewed in VR space, in a table in the desktop portion of the application, and can be exported to Excel or other statistical programs for analysis. The segments themselves can be seamlessly passed to a desktop program like arivis Vision4D for further analysis or can be exported at object files to be used in other programs.

Virtual reality assisted Tracking

Proofreading 3D tracking data and manually adjusting the results is very cumbersome on a 2D screen. Especially, when lots of objects are involved or tracks are very complicated. Using Virtual Reality, you can immerse yourself into the data and gain a detailed view of your sample, that a 2D application cannot match. Entering the dataset and looking at objects and structures as they are surrounding you makes it easier to concentrate on a particular object of interest. With time control on your fingertips, you can always keep your eyes on the object as it moves through the specimen and follows it in space. We developed tools that allow you to add new or edit and delete existing tracks right where you see them - in the image. This makes proofreading tracks faster, easier and more precise than on a desktop.

>>Learn more about Tracking in VR

Interact Seamlessly with Other Platform Products

arivis VisionVR can be used to manually segment structures otherwise impossible to segment automatically Structures that cross each other would normally confuse automatic algorithms. However, our brain is adept at figuring out which piece is part of the correct structure. Because it is possible to visually follow the path of the structure of interest in VR we are able to paint the structure to segment it independently of other parts of the data. Because this segment can be transferred to our desktop arivis Vision4D we can undertake operations like masking out the original data to create a color channel for each segmented region. Then we have the ability to interactively color and turn on and off portions of the original data for presentation and analysis. 

Video: VR masking with arivis VisionVR

Credit: Fly Light Team Project Janelia Research Campus

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