Documentation/Nightly/Extensions/3D Model Segmentation

For the latest Slicer documentation, visit the read-the-docs.

Introduction and Acknowledgements

Extension: 3D_Model_Segmentation Acknowledgments: This work supported in part the National Institutes of Health, National Cancer Institute through the following grants: Quantitative MRI [...] Authors: Andrew Beers (Massachusetts General Hospital) Contact: Andrew Beers, <email>abeers@mgh.harvard.edu</email> License: Slicer License Quantitative Image Informatics for Cancer Research

Module Description

Module description here. Example visualization of MRI/TRUS registration result obtained using this module. Yellow outline is the contour of the manual segmentation of the prostate gland in TRUS, over the registered MRI image of the same prostate.

Use Cases

• Tumor Segmentation

Tutorials

https://www.youtube.com/watch?v=_7oZygGp2ds&t=13s

Documentation

Step 1. Volume Selection Volume Selection: Choose which image you would like to segment. If you only select one image, steps 2 and 3 (Registration, Normalization, and Subtraction) will be skipped. Step 2. Registration (Optional) Registration Order: Choose which order you would like to register your images to. The moving image will be resampled into the space of the fixed image using Slicer's BRAINSFit registration algorithm. Registration Method: Choose which registration method you would like to use. "No Registration" allows you to skip this step. "Rigid Registration" will attempt to rotate and translate the moving image into the space of the fixed image. "Affine Registration" will rotate, translate, scale, and skew the input image. Both affine and rigid registration methods will produce a 3D Slicer Linear Transformation object which you can inspect in the "Transforms" module. "Deformable Registration" will perform deformable BSpline registration on the moving image, and correspondingly create a BSpline Transform object. In general, each method will be more accurate but more time-consuming than the previous method. Registration Output: Either create a new volume for the post-registration moving image, or replace the original image. The new volume will have the name "[VolumeName1]_reg_[VolumeName2]". Step 3. Normalization and Subtraction (Optional) Normalization Methods: Choose the method with which to normalize intensities between your two volumes. This may help to produce better contrast when calculating a subtraction map. Normalization Order: Choose whether to normalize pre- to post-contrast, or vice versa. Step 4. ROI Segmentation of the fixed image Moving image (optional): image to be registered Segmentation of the moving image Registration affine transform: transform to store the result of affine registration Registration deformable transform: transform to store the result of deformable registration Visualization: initializes slice viewers to show overlay of the fixed and moving images (before or after registration), outline of the fixed image segmentation and the registration transform grid and visualization of the fixed and moving segmentation label surfaces in 3d view Similar Modules Volume Clip ChangeTracker References Information for Developers Source code: https://github.com/QTIM-Lab/3D_Model_Segmentation