image_processing6.md

title	Example 6: DICOM RT Visualization in MeVisLab – RTSTRUCT and RTDOSE Workflow
date	2025-05-05
status	OK
draft	false
weight	630
tags	Advanced Tutorial Image Processing DICOM RTSTRUCT RTDOSE RTPLAN
menu	main identifier title weight parent imageprocessing6 Step-by-Step Guide to Loading and Visualizing DICOM RT Data (RTSTRUCT & RTDOSE) in MeVisLab. 630 imageprocessing

Example 6: DICOM RT Visualization in MeVisLab – RTSTRUCT and RTDOSE Workflow

Introduction

This tutorial explains how to load and visualize DICOM RT data in MeVisLab step by step. You will learn how to:

• Load CT and related RTSTRUCT data.
• Visualize RTSTRUCTs as colored CSOs.
• Visualize RTDOSE as a colored overlay.
• Show labels next to each RTSTRUCT contour. We use the ExtractRTStructmodule for this example.

DICOM RT (Radiotherapy) files are essential in radiotherapy treatment planning. They include:

• RTSTRUCT: Defines contours of tumors and organs.
• RTDOSE: Shows planned 3D dose distribution.
• RTPLAN: Contains treatment plan details like beams and dose settings. Together, they ensure accurate and safe radiotherapy delivery.

Prepare your network

First, we need to download the ZIP file at DICOM patient export from:

https://medicalaffairs.varian.com/headandneckbilat-imrtsx2

It contains the CT, RTSTRUCT, and RTDOSE files needed for this tutorial to work correctly in MeVisLab.

{{}} This data is FOR EDUCATIONAL AND SCIENTIFIC EXCHANGE ONLY – NOT FOR SALES OR PROMOTIONAL USE. {{}}

Create a new folder named DICOM_FILES. Extract the ZIP file into this folder.

We will use it in this tutorial.

Add the module DicomImport to your workspace.

Then click Browse and select the new folder named DICOM_FILES where you copied the content of the ZIP file earlier, then press Import. You can see it in the below figure:

Now add a View2D module and connect it to DicomImport.

As shown in the Data Tree (middle pane), the imported DICOM RT structure includes:

• eByLuKOZoWxBUrIW – An anonymized Patient ID.

• 0000-00-00 – Missing or anonymized Birth/Study Date.

When you expand the tree view you will see:

• RTPLAN – Treatment plan metadata (no image).
• RTSTRUCT – Contour data (e.g., organs, tumors; no image).
• CT 512×512×272×1 – The full CT scan volume (visible anatomy).
• RTDOSE 199×115×147×1 – The 3D dose distribution (dose grid).

After connecting, open the View2D and click on each of these items to visualize the corresponding data in the viewer, as shown in the figures below.

{{< imagegallery 4 "/images/tutorials/image_processing/" "RTPLAN" "RTSTRUCT" "CT512" "RTDOSE">}}

Select the CT 512×512×272×1 series. We now want to view the CT images and the RTSTRUCT data together. The module DicomImport only allows to select one single object. In order to select more than one object, we use a DicomImportExtraOutput module. Select the CT series in the DicomImport module and the RTSTRUCT in the DicomImportExtraOutput module.

You have to select the correct index for the RTSTRUCT. In our example it is index 2.

Add DicomImportExtraOutput module as shown in the figure below:

Visualize RTSTRUCTs as colored CSOs:

Now we need an ExtractRTStruct module to convert RTSTRUCT data into CSOs (Contour Segmentation Objects). CSOs allow MeVisLab to visualize the contours on the CT scan and to interact with them.

Then connect it with the DicomImportExtraOutput as shown in the figure:

Add SoView2DCSOExtensibleEditor to enable visualization and interaction with the CSOs in the 2D viewer, allowing you to edit and work with the contours directly. Connect it with View2D and ExtractRTStruct modules, as shown, where View2D displays the CT scan with the contours.

There are no names for the contours by default, we want to show the names for the contour to identify the segmented structure, so we need the CSOLabelRenderer module to add labels (e.g., 'Bladder', 'Prostate') next to each contour, helping to clearly identify the anatomy. The figure below shows that:

As you can see, the contours are labeled with numbers. We want to show the names for the contour to identify the segmented structure. To do this, open the CSOLabelRenderer panel — it will display the panel shown below.

Now, add this line to make the numbers names for the contour: {{< highlight >}}

labelString = cso.getGroupAt(0).getLabel()

{{}}

Then press apply. You can see the names of the structures next to the contours.

3D Visualization of Contours Using SoExaminerViewer

If you want to visualize the contours in 3D, follow these steps:

Add the SoCSO3DRenderer module and connect it to the ExtractRTStruct module. The SoCSO3DRenderer will render the contours (CSOs) in the 3D space.

Add the SoExaminerViewer module and connect it to the SoCSO3DRenderer module. The SoExaminerViewer will allow you to view the 3D contours. You can rotate, zoom, and move around the 3D image.

The following figure shows the network and the result:

Visualizing RTDOSE as a Color Overlay Using LUT

Now we need to add another DicomImportExtraOutput module to import multiple DICOM objects. You have to select the right index for the example it is index 4 thats mean RTDOSE 199115147*1 . Add MinMaxScan module to scan the input image and updates the minimum and maximum values of the output image, and connect it with the DicomImportExtraOutput.

Add Histogram module to calculate the image's intensity distribution, and connect it with MinMaxScan.

The Histogram module computes the image's intensity distribution, and is connected to the SoLUTEditor module to modify the lookup table (LUT). The LUT is then passed to the SoGroup module, which is connected to the SoView2DOverlay module to blend the 2D image overlay in a 2D viewer. The SoLUTEditor module allows interactive editing of the LUT, while SoView2DOverlay module facilitates overlaying the modified image in a 2D scene.

Note that the SoView2DOverlay module is for 2D blending, and the GVROrthoOverlay module should be used for OrthoView2D.

The below figure shows the connections of the network:

Now we should update the Histogram module by pressing the update button on it.

Now open SoLUTEditor module panel, go to Range, Update Range From Histogram to apply the histogram values, as shown in the figure:

Then in the same panel from (Editor... change the color of the values), as shown in the figure:

Finally, when you press View2D, it will display a 2D anatomical image with a colored RTDOSE overlay, where the dose distribution is visualized using a customized Lookup Table (LUT) that clearly highlights the radiation intensity levels within the body.

Summary

• Load DICOM RT data including RTDOSE and RTSTRUCT.
• Visualize RTSTRUCTs with ExtractRTStruct and CSOLabelRenderer.
• Display contour labels using CSOLabelRenderer.
• View images in 2D (View2D) and 3D (SoExaminerViewer).
• Visualize RTDOSE as a color overlay using SoLUTEditor.
• Adjust dose intensity with Histogram and MinMaxScan.

{{< networkfile "/examples/image_processing/example6/DICOMRT.mlab" >}}