Visualization of 3D Data Obtained by the 3D Cell Explorer

It is crucial to be able to visualize tomographic data efficiently if advanced 3D image analysis is to be carried out. This task is one which requires some sophisticated visualization modes, with simple camera control and precise interfaces for transfer function, with excellent control of the transparency. This article discusses how data from the 3D Cell Explorer can be properly exported, prepared, and visualized in the best method. This involves knowing how to use two open source software programs, namely, the 3D ImageJ suite and Tomviz.


In 3D imaging, the visualization of 3D data in simple ways which have sufficient power to show the complexity of a dataset at first sight, as shown in Figure 1, remains a big issue. There are a number of dedicated solutions presented in the academic or the private sector, of which two open source software programs are described here. These are the leading contenders, namely, the 3D ImageJ suite of FIJI (Schmid, Schindelin, Cardona, Longair, & Heisenberg, 2010) and Tomviz, which is designed for tomographic visualization (

Visualization in Tomviz ( of a monolayer growing mouse embryonic stem cells aquired with the 3D Cell Explorer

Figure 1. Visualization in Tomviz ( of a monolayer growing mouse embryonic stem cells acquired with the 3D Cell Explorer


In order to begin the process of creating good-quality 3D visualizations of experimental data, three software programs must be installed. The first is the current version of STEVE which is used to control the 3D Cell Explorer; the second is the current version of FIJI (Schindelin et al., 2012; Schneider, Rasband, & Eliceiri, 2012) ( along with the 3D ImageJ suite, shown in Figure 2, while the third is the current version of Tomviz (

FIJI is updated to get the 3D ImageJ suite. To do this, click Help>Update… as in Figure 2, steps 1-2. Click manage update as in Figure 2, step 3. Perhaps you may be prompted for one or two update rounds by FIJI. Once these are complete, a restart is done (typically once, but sometimes more) until the process can be carried out by just checking the 3D ImageJ suite option as shown in Figure 2, step 4.

Now it is closed as in Figure 2, step 5, FIJI is restarted and 3D as well as 3D viewer are verified to be present in the Plugin menu.

Installation of the 3D ImageJ suite

Figure 2. Installation of the 3D ImageJ suite

Lastly, a good acquisition file is required, which is created using the 3D Cell Explorer. Since a good acquisition is the key to excellent visualizations, optimal imaging conditions must be ensured. For help with this, follow the guidelines at or contact the manufacturer at

Exporting Data

After the dataset has been acquired, the next step is exporting data using the right format. Both the programs for visualization deal with stacks of images under the .tiff format. STEVE has an exporting tool which is used to export a dataset as an anyname.tiff stack. Once acquired, the dataset should be loaded into STEVE. If not, go to File>Export as in Figure 3, steps 1-2. With the dataset loaded, a panel opens, in which the format must be changed to Tiff as shown in Figure 3, step 3.

The file is given a location and a name before exporting it, shown in Figure 3, steps 4-5. The outcome of this export has 96 slices comprising the whole of the Z-dimension acquired. Some may not have useful data regarding the sample, and will contribute to noise in the 3D visualization instead. These must therefore be eliminated from the anyname.tiff file before it is opened in a tool to perform 3D visualization.

Exporting .tiff from STEVE

Figure 3. Exporting .tiff from STEVE

Cutting Out Unwanted Z-Slices

Here FIJI is started, the anyname.tiff file is dragged and dropped in the ImageJ panel, or it can be opened using the open function. Now the slices that make up the anyname.tiff file can be navigated.

Once a slice with the right signal, that is, the best focus, is found, the display thresholds can be adjusted. Go to Image>Adjust>Window/Level, and then click Auto, which will show the signal with best dynamic attributes, as seen in Figure 4, steps 1-4.

Thresholding with FIJI

Figure 4. Thresholding with FIJI

Next, the numbers of the first and the last slices that contain this signal are noted. Now click Image>Stack>Tools>SliceKeeper so that the boundaries of a new image stack can be entered. The first and last slice numbers mentioned previously are now defined along with an increment of 1. This leads to the generation of a new stack, to be saved as anyname_trim.tiff, or something similar. This is seen in Figure 5, steps 1-3. This file can now be opened using the 3D viewer in Tomviz or FIJI.

Creation of an optimal .tiff stack for visualization

Figure 5. Creation of an optimal .tiff stack for visualization

3D Visualization in FIJI

Loading a .tiff file in FIJI’s 3D viewer

Figure 6. Loading a .tiff file in FIJI’s 3D viewer

The file anyname_trim.tiff is opened. Now go to Plugin>3D viewer as seen in Figure 6, steps 1-2. If asked whether you want to transform into 8-bit data, click yes, and even if text in red appears, it may be ignored. Finally, in the resampling factor, put in 1 for powerful computers but otherwise leave it at 2.

This will make the image less detailed but put less strain on the computer, as shown in Figure 6, step 3. While this visualization option has less power, it has advanced analysis functionalities because of the installed ImageJ suite.

3D Visualization in Tomviz

How to Load and add a Volume Visualization

The file anyname_trim.tiff in Tomviz is opened as File>Open Data. The dataset will be seen in the left-sided column as shown in Figure 7. Tomviz relies on workflow logic and therefore various treatments must be applied to the dataset to allow it to be visualized as per need. The first treatment is a volume visualization added as Visualization>Volume, shown in Figure 7, steps 1-3, and then comes activation of the function enable gradient opacity as in Figure 7, step 4.

Visualizing a .tiff stack as volume in Tomviz

Figure 7. Visualizing a .tiff stack as volume in Tomviz

In Tomviz 1.0 and later, this option is activated by default, and therefore it cannot be accessed. Another point to be noted is the need to inactivate the orthogonal slice that will superpose on the view of the volume if this is not done, as seen in Figure 7, step 5.

The Visualization menu contains a set of view modes which should be gone through and tested. Errors are easily corrected by removing unnecessary steps from the pipeline column, to return to the older better state.

Changing Color Map and Transfer Function Adaption

At this point the manner of volume display may be changed. Go to the right and click the colormap folder. From this, any desired one can be selected. Here the Blue to Red Rainbow is used as seen in Figure 8, steps 1-3.

Color changes are seen with volume as well as the histogram changes color at this point. Figure 8 shows the histogram.

Adapting colormap and transfer function in Tomviz

Figure 8. Adapting colormap and transfer function in Tomviz

The panel at upper middle shows how the signal is distributed and its scale variation with color. The straight line that is seen over it shows the transfer function, or the mode of transfer of the signal for display, as seen in Figure 8, step 4.

It is advised that a single point is added on the transfer function by one click on the line, and then to decrease the transfer function down to zero to the point where the meaningful signal is no longer seen. This reduces even more of the background signal.

The color map should then be scaled by the addition of a cursor point on the left, which is then dragged to below the transfer function point, or close to it. This is shown in Figure 8, step 5. Throughout the color adjustment process, the volume must be observed at different angles in the lower middle panel.

By now you might have a volume that looks nice. Now the colormapping is adjusted along with the transfer function to refine the rendering. The instruction manual from Tomviz is clear and detailed and will help immensely in achieving good visualization. Go to Help>User Guide.


Schindelin, J., Arganda-Carreras, I., Frise, E., Kaynig, V., Longair, M., Pietzsch, T., … Cardona, A. (2012). Fiji: an open-source platform for biological-image analysis. Nature Methods, 9(7), 676–682.

Schmid, B., Schindelin, J., Cardona, A., Longair, M., & Heisenberg, M. (2010). A high-level 3D visualization API for Java and ImageJ. BMC Bioinformatics, 11(1), 274.

Schneider, C. A., Rasband, W. S., & Eliceiri, K. W. (2012). NIH Image to ImageJ: 25 years of image analysis. Nature Methods, 9(7), 671–675.

About Nanolive SA

Nanolive SA are scientists, working for scientists.

Their belief is that each and every Biologist, Researcher and Physician should be able to explore and interact instantly with living cells without damaging them.

Nanolive want to support the study of how living cells and bacteria work, evolve and react, thus building a solid base for new drugs and therapies, in order to enable breakthrough researches.

This is the reason why they have developed the 3D Cell Explorer.

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Last updated: Mar 12, 2019 at 4:04 AM


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