ChangeTracker:Lupus DBP

This page summarizes requirements for longitudinal lesion analysis for Lupus DBP, and resulting discussion on possible modifications required to ChangeTracker to make it possibly a more generic change analysis tool.

Longitudinal lesion analysis for Lupus project

Lupus DBP will face the need of analyzing multi-modal same-subject brain imaging data at 3+ timepoints in order to detect white matter lesions and analyze their progression. At each time point the following data will be available:

• T1
• T2
• FLAIR
• results of lesion classification: (1) labeled connected component filtering of classification/segmentation output + (2) per-label threshold information for each of the connected components

Essentially, pre-thresholded classification output is the per-pixel estimation of likelihood that the pixel belongs to the lesion. Threshold is defined on the per-lesion basis, since its selection depends on the tumor location and other properties.

Requirements to the analysis results are:

• ability to accept multiple lesions, but give control over per-lesion analysis
• ability to go through the available lesion modalities
• CompareView mode for rsults visualization
• naturally, no need for lesion detection functionality
• ability to adjust the classification threshold (per-lesion): should this be done separately for each timepoint?
• distance-based coloring of the change results
• ability to save/load analysis results

Consequences for ChangeTracker

Currently, ChangeTracker is essentially designed for change detection in a specific clinical application: meningioma imaging. There may be a need to refactor ChangeTracker to separate out the segmentation functionality from the change detection functionality, to make it more generic.

We can also try to develop two separate workflows within ChangeTracker for Meningioma and Lupus projects, since separating generic change detection functionality may be hard/not possible.

Mock-up workflow for Lupus analysis:

• Step 1