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Background: Visualization of multi-channel microscopy data plays a vital rolein biological research. With the ever-increasing resolution of modernmicroscopes the data set size of the scanned specimen grows steadily. Oncommodity hardware this size easily exceeds the available main memory and theeven more limited GPU memory. Common volume rendering techniques require theentire data set to be present in the GPU memory. Existing out-of-core renderingapproaches for large volume data sets either are limited to single-channelvolumes, or require a computer cluster, or have long preprocessing times.Results: We introduce a ray-casting technique for rendering large volumetricmulti-channel microscopy data streams on commodity hardware. The volumetricdata is managed at different levels of detail by an octree structure. Incontrast to previous octree-based techniques, the octree is built incrementallyand therefore supports streamed microscopy data as well as data set sizesexceeding the available main memory. Furthermore, our approach allows the userto interact with the partially rendered data set at all stages of the octreeconstruction. After a detailed description of our method, we presentperformance results for different multi-channel data sets with a size of up to24 GB on a standard desktop PC. Conclusions: Our rendering technique allowsbiologists to visualize their scanned specimen on their standard desktopcomputers without high-end hardware requirements. Furthermore, the user caninteract with the data set during the initial loading to explore the alreadyloaded parts, change rendering parameters like color maps or adjust clippingplanes. Thus, the time of biologists being idle is reduced. Also, streamed datacan be visualized to detect and stop flawed scans early during the scanprocess.

Visualization of Large Volumetric Multi-Channel Microscopy Data Streams on Standard PCs