Modules:FastMarchingSegmentation-Documentation-3.5

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Fast Marching Segmentation

FastMarchingSegmentation

Initialization of the algorithm with fiducial point

Adjusted segmentation label and volume rendering

Supported by BrainScienceFoundation

General Information

Module Type & Category

Type: Interactive

Category: Segmentation

Authors, Collaborators & Contact

• Andriy Fedorov, BWH
• Eric Pichon
• Contact: Andriy Fedorov, fedorov at bwh

Module Description

This module implements segmentation based on fast marching algorithm.

Usage

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.

Initialization: The user is required to specify the following items to initialize the segmentation process: Input volume: image that contains the structure to be segmented Input seeds: fiducial list containing fiducials marking points within the region to be segmented. Output volume: output label volume that will keep the segmentation result Target segmented volume: positive number that estimates expected volume of the segmented structure in milliliters (mL) Output label value: value that will be assigned to the non-zero pixels of the segmentation result Once these items have been specified, push Run Segmentation button to initiate the segmentation process. Adjustment of the segmentation result: The segmentation process can be described on the high level as evolution of contours, that starts from the seed points provided in the initialization step. In the current adjustment step, the user can go back in the evolution of the segmentation contour by changing the current value of the volume for the segmented structure. This process is facilitated by the following controls: Segmented volume selection range: This range slider allows to specify the range of volumes that you want to experiment with in adjusting the result. The maximum range is from 0 to the target segmented volume specified in the previous step. Output segmentation volume:: This slider allows to smoothly scroll through the volume values within the selected range. The segmentation contour is interactively updated in the slice view as the slider is moved. Note, that as you lock in to the most desirable segmentation result, you would reduce the selection range to allow for better precision of contour adjustment. Once the satisfactory result is achieved, Accept Segmentation Result button will finalize the segmentation.

Control panel for FastMarching initialization Control panel for FastMarching result adjustment

Development

Features under development:

• add volume rendering -- DONE
• add range control widget to simplify result adjustment -- DONE
• add progress reporting -- DONE
• add ability to initialize with label, not only with fiducials
• documentation
• simplify output label initialization process (selector widget) -- DONE
• integration with Editor
• switch active fiducial list when a new one is added -- DONE
• add parameter node
• Ron: specify the target segmented volume in either mm^3 or cm^3 or major dimensions (1x2x4cm)
• add volume rendering on/off capability -- DONE

Dependencies

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

Known bugs

Follow this link to the Slicer3 bug tracker.

• Execution time of one of the algorithm components is O(max_image_intensity^2). A temporary workaround if the execution time is not acceptabe is to rescale the image to have intensity range of about 600 for reasonable performance. In the future, this step will be done automatically by the implementation.

Usability issues

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.

More Information

Acknowledgment

Supported by Brain Science Foundation.

References

• Pichon E, Tannenbaum A, Kikinis R. A statistically based flow for image segmentation. Med Image Anal. 2004 Sep;8(3):267-74. PMID: 15450221. [1]