SlicerCineTrack: An open-source research toolkit for target tracking verification in 3D Slicer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it