Fast Marching Segmentation
Module Type & Category
Authors, Collaborators & Contact
- Andriy Fedorov, BWH
- Eric Pichon
- Contact: Andriy Fedorov, fedorov at bwh
This module implements segmentation based on fast marching algorithm.
Examples, Use Cases & Tutorials
- This module is most useful to segment image regions that have similar intensity
- Initialization is very simple: points within the region to be segmented and expected volume of the segmented structure
- The segmentation is completed relatively quickly for typical images, facilitating experimentation with the selection of optimum parameters
- The resulting volume can be adjusted interactively by scrolling through the evolution of the label contour
Quick Tour of Features and Use
Segmentation workflow of this module consists of the two steps: Initialization and adjustment of the segmentation result.
Once these items have been specified, push Run Segmentation button to initiate the segmentation process.
Once the satisfactory result is achieved, Accept Segmentation Result button will finalize the segmentation.
- Put bigger number as the estimate volume It is better to overestimate the volume of the structure in the initialization, than underestimate. Remember, that you can always scroll back in the contour evolution, but cannot exceed predefined volume limit.
- Play with the fiducial points If the segmentation result is not satisfactory, try adding more fiducials. If the region to be segmented has regions of different intensities, put fiducials in each of such regions. If all fails, try to segment separate regions one after another, and use ImageLabelCombine module to merge them together later.
- Take advantage of volume rendering You can quickly see if there are "leaks", without the need to scroll through the slices. However, volume rendering may not show small "leaks".
Some of the desired features:
- add ability to initialize with label, not only with fiducials
- integration with Editor
- add parameter node
- Ron: specify the target segmented volume in either mm^3 or cm^3 or major dimensions (1x2x4cm)
Follow this link to the Slicer3 bug tracker.
Follow this link to the Slicer3 bug tracker. Please select the usability issue category when browsing or contributing.
Source code & documentation
Source code can accessed here
Links to documentation generated by doxygen.
- Pichon E, Tannenbaum A, Kikinis R. A statistically based flow for image segmentation. Med Image Anal. 2004 Sep;8(3):267-74. PMID: 15450221.