Documentation/4.3/Extensions/ChangeTracker

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Introduction and Acknowledgements

This work is supported by NA-MIC, NAC, NCIGT, and the Slicer Community. This work is partially supported by Brain Science Foundation and NIH U01 CA151261. Author: Andrey Fedorov, Kilian Pohl, Peter Black, Ron Kikinis, SPL Contact: Andrey Fedorov <email>fedorov@bwh.harvard.edu</email>

National Alliance for Medical Image Computing (NA-MIC)   Brain Science Foundation (BSF)   National Center for Image Guided Therapy (NCIGT)   Surgical Planning Laboratory (SPL)

Module Description

ChangeTracker is a software tool for quantification of the subtle changes in pathology. The module provides a workflow pipeline that combines user input with the medical data. As a result we provide quantitative volumetric measurements of growth/shrinkage together with the volume rendering of the tumor and color-coded visualization of the tumor growth/shrinkage. ChangeTracker is not yet a generic tool for detecting small changes. It has been tuned specifically for meningioma growth analysis. Specifically, the assumptions made by ChangeTracker are the analyzed images are roughly aligned the pathology area is characterized by bright image intensities (contrast-enhanced meningioma MRI) images have similar, close to isotropic, spacing the change in pathology between the consequtive scans is small in relation to the total pathology volume. If you want to apply ChangeTracker on non-meningioma data, please let us know. The algorithm is likely to require tuning of the parameters for registration and other processing steps.

ChangeTracker processing results visualization

Use Cases

Most frequently used for these scenarios:

• Quantification of small changes in meningioma tumor volume from post-contrast MRI

Tutorials

The test data can be downloaded automatically by pushing the "Load test data" in the panel of the first step (you will need the internet connection).

Panels and their use

ChangeTracker is organized as a workflow that consists of the following steps:

• Step 1: Define input scans

Use drop-down controls to choose the two scans where you would like to measure pathology development. Currently, we support analysis of the images that correspond to two time points.

Step 1: Scan selection

• Step 2: Define volume of interest

This step of wizard includes the following user controls to facilitate Volume of Interest (VOI) selection: "Hide/show render" button: used to control visibility of volume rendering for the selected region "ROI Widget Controls: RAS Space" frame: contains sliders to initialize VOI in RAS (physical) space "ROI Widget" refers to the three-dimensional selection box that appears in the 3d slice view once you begin to select VOI. You can define VOI by adjusting the colored handles of the ROI widget in the slice viewer or 3d viewer.

Step 2: ROI Widget controls in wizard GUI

• Step 3: Segment the analyzed structure

Use threshold control slider to find the intensity that most closely approximates tumor volume. Thresholded volume is rendered interactively in the 3D viewer as you are adjusting the threshold value, and is also visualized as semi-transparent label in the image slice viewers. Note, that currently ChangeTracker expects that the tissue you monitor is hyperintensive on the image. In the cases when threshold is not effective in segmenting the structure of interest, Advanced tab allows to prescribe the segmentation label image directly, instead of using threshold. (this feature is currently disabled, under development)

Step 3: ChangeTracker ROI segmentation

• Step 4: ROI Analysis

Choose the metric(s) you would like to use. ChangeTracker provides an extensible framework for developing and incorporating change quantification metrics into the workflow (see Information for Developers section). The metrics currently available are the following (follow the links for details and documentation): Intensity Difference Metric is based on intensity difference between the two ROIs In some cases, the registration procedure that ChangeTracker is using may not be robust enough to align your data. If this happens and the baseline and followup ROIs are not aligned after this step, you can use the Advanced tab to register your data, and place followup volume under the transform. ChangeTracker will use the prescribed transform and will skip registration step.

Step 4: ROI Analysis

• Step 5: ROI Analysis Results

Results are reported as the change in tumor volume, separately for growth and shrinkage component. The quantitative results are reported in voxels, mL and percentage relative the the volume of the structure segmented in the baseline scan. The visualization of the analysis results includes the following components upon the completion of analysis: Red slice view: resampled VOI for the second of the analyzed time-points 3d slice viewer: color-coded results of the change analysis. Red color corresponds to the estimated growth regions, Green corresponds to estimated shrinkage. Compare view: first row contains the resampled ROI corresponding to the first time-point as foreground, with the growth analysis results in the background. The second row contains resampled ROI corresponding to the second time-point.

Step 5: ROI Analysis Results

Similar Modules

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References

• Konukoglu, E., Wells, W. M., Novellas, S., Ayache, N., Kikinis, R., Black, P. M., & Pohl, K. M. (2008). Monitoring slowly evolving tumors. 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro (pp. 812-815). IEEE. doi:10.1109/ISBI.2008.4541120 URL
• Pohl, K. M., Konukoglu, E., Novellas, S., Ayache, N., Fedorov, A., Talos, I.-F., Golby, A., et al. (2011). A new metric for detecting change in slowly evolving brain tumors: validation in meningioma patients. Neurosurgery, 68(1 Suppl Operative), 225-33. doi:10.1227/NEU.0b013e31820783d5 URL

Information for Developers

• Source code: https://github.com/fedorov/ChangeTrackerPy