Example for DICOMRT

mevislab.github.io/content/tutorials/image_processing/image_processing6.md

@@ -0,0 +1,170 @@
+---
+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: "imageprocessing6"
+    title: "Step-by-Step Guide to Loading and Visualizing DICOM RT Data (RTSTRUCT & RTDOSE) in MeVisLab."
+    weight: 630
+    parent: "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 `ExtractRTStruct`module 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.
+
+{{<alert class="warning" caption="Attention">}}
+This data is FOR EDUCATIONAL AND SCIENTIFIC EXCHANGE ONLY – NOT FOR SALES OR PROMOTIONAL USE.
+{{</alert>}}
+
+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. 
+
+!["DicomImport](/images/tutorials/image_processing/Dicommm.png "DicomImport")
+
+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:
+
+![Importing DICOM RT Data from the DICOM_FILES Folder](/images/tutorials/image_processing/Import.png "Importing DICOM RT Data from the DICOM_FILES Folder")
+
+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:
+
+![Selecting CT and RTSTRUCT in DicomImport](/images/tutorials/image_processing/SELECTINGCTRTSTRUCT.png "Selecting CT and RTSTRUCT in DicomImport")
+
+### **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:
+
+![ExtractRTStruct vonverter](/images/tutorials/image_processing/converter.png "ExtractRTStruct converter")
+
+
+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.
+
+![Connecting SoView2DCSOExtensibleEditor](/images/tutorials/image_processing/cotour.png "Connecting SoView2DCSOExtensibleEditor")
+
+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:
+
+![CSOLabelRenderer](/images/tutorials/image_processing/CSOLabelRenderer.png " CSOLabelRenderer")
+
+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.
+
+
+![CSOLabelRenderer panel](/images/tutorials/image_processing/CSOLabelRendererpanel.png "CSOLabelRenderer panel")
+
+Now, add this line to make the numbers names for the contour:
+{{< highlight >}}
+```Python
+labelString = cso.getGroupAt(0).getLabel()
+```
+{{</highlight>}}
+
+Then press apply. You can see the names of the structures next to the contours.
+
+![Edited CSOLabelRenderer Panel](/images/tutorials/image_processing/contournamed.png " Edited CSOLabelRenderer Panel")
+
+### **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: 
+
+![3D Visualization of Contours Using SoExaminerViewe](/images/tutorials/image_processing/3D.png "3D Visualization of Contours Using SoExaminerViewe")
+
+### **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 199*115*147*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`.
+
+![Importing RTDOSE Data and Applying MinMaxScan for Image Normalization](/images/tutorials/image_processing/minmaxscan.png "Importing RTDOSE Data and Applying MinMaxScan for Image Normalization")
+
+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:
+
+![Workflow for Intensity Distribution and 2D Image Overlay using LUT Editing"](/images/tutorials/image_processing/net.png "Workflow for Intensity Distribution and 2D Image Overlay using LUT Editing")
+
+Now we should update the `Histogram` module by pressing the update button on it.
+
+![Update Histogram](/images/tutorials/image_processing/histo.png " Update Histogram")
+
+Now open `SoLUTEditor` module panel, go to *Range*, *Update Range From Histogram* to apply the histogram values, as shown in the figure:
+
+![SoLUTEditor Panel](/images/tutorials/image_processing/solut.png "SoLUTEditor Panel")
+
+Then in the same panel from (*Editor*... *change the color of the values*), as shown in the figure: 
+
+![SoLUTEditor Panel Editior](/images/tutorials/image_processing/editior.png " SoLUTEditor Panel Editior")
+
+
+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.
+
+![Visualizing RTDOSE as a Color Overlay Using LUT](/images/tutorials/image_processing/2df.png "Visualizing RTDOSE as a Color Overlay Using LUT")
+
+## **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" >}}

mevislab.github.io/content/tutorials/shorts.md

@@ -35,42 +35,53 @@ This is a collection of useful keyboard shortcuts in MeVisLab, hopefully it grow
   </thead>
   <tbody>
     <tr>
-      <td>Ctrl+1</td>
+      <td>{{< keyboard "CTRL" "1" >}}</td>
       <td>Automatically arrange selection of modules / in the current network</td>
     </tr>
     <tr>
-      <td>Ctrl+2</td>
+      <td>{{< keyboard "CTRL" "2" >}}</td>
       <td>Open most recent network file</td>
     </tr>
     <tr>
-      <td>Ctrl+3</td>
+      <td>{{< keyboard "CTRL" "3" >}}</td>
       <td>Run most recent test case (extremely useful for developers)</td>
     </tr>
-      <td>Ctrl+A then Ctrl+1</td>
+    <tr>
+      <td>{{< keyboard "CTRL" "A" >}} then {{< keyboard "CTRL" "1" >}}</td>
       <td>Layout network</td>
     </tr>
-      <td>Ctrl+A then Tab</td>
+    <tr>
+      <td>{{< keyboard "CTRL" "A" >}} then {{< keyboard "TAB" >}}</td>
       <td>Layout .script file (in MATE)</td>
     </tr>
-      <td>Ctrl+D</td>
+    <tr>
+      <td>{{< keyboard "CTRL" "D" >}}</td>
       <td>Duplicate currently selected module (including all field values)</td>
     </tr>
-      <td>Ctrl+Left Mouse or Middle Mouse Button</td>
+    <tr>
+      <td>{{< keyboard "CTRL" >}} and Left Mouse {{< mousebutton "left" >}} or Middle Mouse Button {{< mousebutton "middle" >}}</td>
       <td>Show Internal Network</td>
     </tr>
-      <td>Space</td>
+    <tr>
+      <td>{{< keyboard "SPACE" >}}</td>
       <td>Show hidden outputs of the currently selected module</td>
     </tr>
-      <td>Ctrl+Alt+T</td>
+    <tr>
+      <td>{{< keyboard "CTRL" "ALT" "T" >}}</td>
       <td>Start test center</td>
     </tr>
-      <td>Ctrl+K	</td>
+    <tr>
+      <td>{{< keyboard "CTRL" "K" >}}</td>
       <td>Restart MeVisLab with current network(s)</td>
     </tr>
-    </tr>
-      <td>Ctrl+R</td>
+    <tr>
+      <td>{{< keyboard "CTRL" "R" >}}</td>
       <td>Run script file with the same name of your network file if available in the same directory.</td>
     </tr>
+    <tr>
+      <td>{{< keyboard "ALT" >}} Double-click {{< mousebutton "left" >}} on a module</td>
+      <td>Open automatic panel of the module.</td>
+    </tr>
   </tbody>
 </table>
 

mevislab.github.io/hugo.toml

@@ -28,9 +28,9 @@ canonifyURLs = true
         title = true
 
 [params]
-  [params.author]
+  copyrightyear = '{year}'
+  [params.Author]
     name = "MeVis Medical Solutions AG"
-    copyright = "{year}"
     website = "https://www.mevis.de"
 
 [module]

mevislab.github.io/themes/MeVisLab/layouts/partials/footer.html

@@ -1,7 +1,7 @@
 <footer class="footer fixed-bottom mt-auto py-3">
 
     <div class="row footer-text">
-      <div class="col-12 col-md text-center text-md-left text-white">&copy; Copyright {{ replace .Site.Author.copyright "{year}" now.Year }} <a href="{{ .Site.Author.website }}" target="_blank">{{ .Site.Author.name }}</a> | <a href="https://www.mevislab.de/imprint" target="_blank">Imprint</a> | <a href="https://www.mevislab.de" target="_blank">https://www.mevislab.de</a></div>
+      <div class="col-12 col-md text-center text-md-left text-white">&copy; Copyright {{ replace .Site.Params.copyrightyear "{year}" now.Year }} <a href="{{ .Site.Params.Author.website }}" target="_blank">{{ .Site.Params.Author.name }}</a> | <a href="https://www.mevislab.de/imprint" target="_blank">Imprint</a> | <a href="https://www.mevislab.de" target="_blank">https://www.mevislab.de</a></div>
     </div>
 
   <!-- JavaScript and dependencies -->

mevislab.github.io/themes/MeVisLab/layouts/partials/nextprev.html

@@ -1,7 +1,7 @@
 {{ if eq .Page.Kind "page" }}
     {{ if gt .Page.Weight 0 }}
     <hr />
-    <div class="row">
+    <div class="row" style="margin: 25px !important;">
         {{ with .Site.Pages.ByWeight.Next .}}
         <div class="col">
             <a href="{{ .Permalink }}" title="{{ .Title }}" class="btn btn-outline-secondary btn-sm" role="button" aria-pressed="true">