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 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 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 {{< mousebutton "left" >}} Browse and select the new folder named DICOM_FILES where you copied the content of the ZIP file earlier. Click Import {{< mousebutton "left" >}}. You can see the result after import in the below figure:

Now add a View2D module and connect it to the DicomImport module.

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.

Visualize RTSTRUCTs as colored CSOs

Now we need an ExtractRTStruct module to convert RTSTRUCT data into CSOs (Contour Segmentation Objects). CSOs in MeVisLab allow 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 a SoView2DCSOExtensibleEditor module to enable visualization and interaction with the CSOs in the 2D viewer. Connect it with a View2D module and the ExtractRTStruct module. The View2D module shows the CT scan with the contours from the RTSTRUCT file.

There are no names for the contours shown by default. We want to display the names for the contours available in the RTSTRUCT file to identify the segmented structure. Use the CSOLabelRenderer module to show labels (e.g., 'Bladder', 'Prostate') next to each contour. The figure below shows that:

As you can see, the contours are labeled with numbers. The number is the internal ID of the contour. We want to show the names for the contour to identify the segmented structure. To do this, open the CSOLabelRenderer panel.

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

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

{{}}

Then press apply. The label of the CSO provides the name of each contour. You can see them next to the contours.

{{}} You can use a CSOLabelPlacementLocal module or a CSOLabelPlacementGlobal module to define the locations of these labels. {{}}

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 3D.

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

We now ant to show the RTDOSE data as provided for the patient as a semi-transparent overlay.

Now we need to add another DicomImportExtraOutput module to import multiple DICOM objects. You have to select the correct index for the example. In this case, we select index 4 for the RTDOSE 199115147*1 data. Add a MinMaxScan module to scan the input image and calculate the minimum and maximum values of the image. Connect it with the DicomImportExtraOutput module.

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

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 the SoView2DOverlay module facilitates overlaying RTDOSE in a 2D scene.

{{}} Note that the SoView2DOverlay module is for 2D blending. 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:

In the same panel, on tab Editor, change the colors as shown in the figure:

Finally, when you open the View2D panel, 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" >}}