Modules:SpineSegmentation-Documentation-3.6

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Gallery of New Features

Module Name

SpineSegmentation module for Slicer 3.6

Load volume into Slicer3.6 and select the SpineSegmentation module

Select input and output nodes and click Apply

Processing takes approx. 3 minutes. The segmentation results are shown as a label map (blue) and a 3D model (3D view)

General Information

Module Type & Category

Type: Interactive or CLI

Category: Segmentation

Authors, Collaborators & Contact

• Sylvain Jaume: MIT & logo, if desired
• Contact: Sylvain Jaume, sylvain at csail.mit.edu

Module Description

Image segmentation of the spinal cord and the cerebro-spinal fluid in T2-weighted MRI images. The SpineSegmentation module implements a model-based pattern recognition algorithm for fully automated segmentation.

Usage

Use Cases, Examples

This module is especially appropriate for this use case:

• Automated segmentation of MRI of the spine is critical during minimally invasive intervention when frequent acquisitions of intra-operative images prevents the manual segmentation of the image by an expert. The SpineSegmentation module provides a solution to reduce the burden on the radiologist.

Examples of the module in use:

• Image-guided surgery for the treatment of disk herniation

Tutorial

Links to sample data to use with the module tutorial:

• Tutorial: see section below
  • Data Set

To download the sample data, you need to go to http://nitrc.org and create a username. The data are located at

http://www.nitrc.org/plugins/scmsvn/viewcvs.php/Slicer3/Modules/SpineSegmentation/TestingData/?root=sylvainproject

Below are the step-by-step instructions to load, process and save some sample data (see link above).

Step 1. Load the sample data Input panel: Image input select the input image Image output create Slicer node for output image Model output create Slicer node for output model Command panel: Default reset input and output nodes to blank values Cancel cancel the execution of the algorithm Apply apply the segmentation algorithm (takes approx. 3 min)

1. load the sample data

Step 2/9. Select the SpineSegmentation module Input panel: Image input select the input image Image output create Slicer node for output image Model output create Slicer node for output model Command panel: Default reset input and output nodes to blank values Cancel cancel the execution of the algorithm Apply apply the segmentation algorithm (takes approx. 3 min)

2. select SpineSegmentation module

Step 3/9. Select the input image Input panel: Image input select the input image Image output create Slicer node for output image Model output create Slicer node for output model Command panel: Default reset input and output nodes to blank values Cancel cancel the execution of the algorithm Apply apply the segmentation algorithm (takes approx. 3 min)

3. select input image

Step 4/9. Create a Slicer node for the output image Input panel: Image input select the input image Image output create Slicer node for output image Model output create Slicer node for output model Command panel: Default reset input and output nodes to blank values Cancel cancel the execution of the algorithm Apply apply the segmentation algorithm (takes approx. 3 min)

4. create image node

Step 5/9. Create a Slicer node for the output model Input panel: Image input select the input image Image output create Slicer node for output image Model output create Slicer node for output model Command panel: Default reset input and output nodes to blank values Cancel cancel the execution of the algorithm Apply apply the segmentation algorithm (takes approx. 3 min)

5. create model node

Step 6/9. Apply the segmentation algorithm Input panel: Image input select the input image Image output create Slicer node for output image Model output create Slicer node for output model Command panel: Default reset input and output nodes to blank values Cancel cancel the execution of the algorithm Apply apply the segmentation algorithm (takes approx. 3 min)

6. apply algorithm

Step 7/9. Review segmentation result Input panel: Image input select the input image Image output create Slicer node for output image Model output create Slicer node for output model Command panel: Default reset input and output nodes to blank values Cancel cancel the execution of the algorithm Apply apply the segmentation algorithm (takes approx. 3 min)

7. review result

Step 8/9. Save result image and model Input panel: Image input select the input image Image output create Slicer node for output image Model output create Slicer node for output model Command panel: Default reset input and output nodes to blank values Cancel cancel the execution of the algorithm Apply apply the segmentation algorithm (takes approx. 3 min)

8. save results

Step 9/9. Find contact information for help and paper reference Input panel: Image input select the input image Image output create Slicer node for output image Model output create Slicer node for output model Command panel: Default reset input and output nodes to blank values Cancel cancel the execution of the algorithm Apply apply the segmentation algorithm (takes approx. 3 min)

9. help information

Development

Notes from the Developer(s)

Algorithms used, library classes depended upon, use cases, etc.

Dependencies

Other modules or packages that are required for this module's use.

Tests

On the Dashboard, these tests verify that the module is working on various platforms:

• MyModuleTest1 MyModuleTest1.cxx
• MyModuleTest2 MyModuleTest2.cxx

Known bugs

Links to known bugs in the Slicer3 bug tracker

• Bug 000: description

Usability issues

Follow this link to the Slicer3 bug tracker. Please select the usability issue category when browsing or contributing.

Source code & documentation

Links to the module's source code:

Source code:

• file.cxx
• file.h

Doxygen documentation:

• class1

More Information

Acknowledgment

Include funding and other support here.

References

Publications related to this module go here. Links to pdfs would be useful.