SlicerCineTrack: An open-source research toolkit for target tracking verification in 3D Slicer
Bibliographic record
Abstract
Target motion monitoring plays a significant role in several computer-assisted interventions. However, ensuring the reliability of tracking algorithms can be challenging without adequate tools. We introduce SlicerCineTrack, a free open-source research toolkit, designed to provide users with a user-friendly interface for visualizing their target tracking results. SlicerCineTrack was developed as an extension to 3D Slicer. It enables users to visualize target tracking results by sequentially playing back cine medical images, and simultaneously overlaying the target segmentation at the locations indicated by the tracking results. The extension was evaluated by established experts in computer-assisted interventions and image-guided therapy. SlicerCineTrack is available for download from the 3D Slicer extension catalog for stable releases, and its GitHub repository for preview releases. Evaluation results demonstrate SlicerCineTrack’s effectiveness in discriminating between different tracking performances. Moreover, the experts found the extension convenient to use due to its intuitive and user-friendly interface. SlicerCineTrack was found to be effective at verifying the reliability of tracking algorithms. In turn, SlicerCineTrack shows potential for target tracking verification, as well as algorithm validation and refining through parameter tuning. • Open-source toolkit for target tracking verification in 3D Slicer. • Enables visualization of tracking results with cine images and segmentation overlays. • Evaluated by experts; found effective and user-friendly for tracking performance. • Facilitates algorithm validation and refinement. • Extends 3D Slicer functionalities to support cutting-edge research in CAI.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.075 | 0.043 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".