Volume rendering of Voxel based data

Deborah Schmidt

Helmholtz Imaging | MDC Berlin

Sep 25, 2024

Slides available at https://ida-mdc.github.io/workshop-visualization/1-3_in-depth-voxels/

Visualizing volumetric datasets

Slice-Based Visualization: This involves rendering 2D cross-sections or “slices” of the 3D dataset, often used in medical imaging.
Volume raycasting (max intensity, emission absorbtion)

Slicing, Max. Intensity, Emission Absorbtion

Visualizing Voxel Data interactively with napari

Install and activate environment (guide)
Download Notebook voxel_rendering_napari.ipynb into workshop directory
Type jupyter lab and press Enter
Open Notebook from list of files on the left side
Run Cells in the Notebooks one by one by pressing Shift and Enter

Visualizing volumetric datasets

Transfer functions

When working with unannotated volumetric datasets, you can explore the data interactively using transfer functions. Transfer functions map intensity values in the dataset to colors and opacities, allowing you to visualize different regions of the volume without defining hard boundaries. This technique is often used for soft, exploratory visualizations of the internal structures of the data.

Transfer Functions:

A transfer function defines how data values are mapped to colors and transparency.
Example: Low intensity values may be mapped to transparent regions, while higher intensities are mapped to visible colors.
Transfer functions are typically adjusted in visualization software like ParaView.
By adjusting transfer functions, you can emphasize specific parts of the volume without needing concrete borders or segmentations.

Figure by Stefan Bruckner from the following publication: Bruckner, Stefan & Gröller, Eduard. (2007). Style Transfer Functions for Illustrative Volume Rendering. Computer Graphics Forum. 26. 715 - 724. 10.1111/j.1467-8659.2007.01095.x.

Rendering with VTK including Transfer Function adjustment

Install and activate environment (guide)
Download Notebook voxel_rendering_vtk.ipynb into workshop directory
Type jupyter lab and press Enter
Open Notebook from list of files on the left side
Run Cells in the Notebooks one by one by pressing Shift and Enter

Big Data Rendering

How can images be loaded partially and on demand?

Image stored in chunks as files on disk
Image stored in different resolutions
Open Microscopy Environment specification format (OME-NGFF)

Big Data Rendering

BigDataViewer Ecosystem

Supports large data formats: The BDV ecosystem can handle massive 3D datasets and allow arbitrary slicing.

Separate tutorial - click this box!

Volume rendering with BigDataViewer tools

Learn how to render voxel-based volumetric data using BigDataViewer (BDV) and tools built on top of BDV.

Big Data Rendering

Streaming data locally

With Python:

Step 1: Open your terminal and activate the workshop environment
Step 2: Download this script somewhere convenient.
Step 3: Navigate to your data: cd workshop
Step 4: Run the script: python server.py
Step 2: Open the Neuroglancer demo page and enter the local URL of your data (e.g., zarr://http://localhost:8000/my-dataset.ome.zarr).

Big Data Rendering

Providing data

Open https://ome.github.io/ome-ngff-validator/
Either open one of their example URLs, or..
.. attach your data URL to the validator URL: https://ome.github.io/ome-ngff-validator/?source=http://0.0.0.0:8080/my-dataset.ome.zarr

Visualizing volumetric datasets

Web based rendering with Neuroglancer

Collaboration-friendly: Share URLs with collaborators to provide access to the 3D visualization.

Separate tutorial - click this box!

Volumetric data rendering with Neuroglancer

Use case description of how to render voxel-based volumetric data using Neuroglancer and stream data locally or remotely for visualization.