Efficient real-time 3D tracking of liver targets through image registration and LightGBM
Bibliographic record
Abstract
Background: The ability to track regions of interest in real time is essential for many clinical procedures. However, many systems are constrained to 2D tracking and experience delays due to image acquisition and processing times. This research presents innovative techniques to estimate 3D target movements in real time by utilizing interleaved coronal and sagittal magnetic resonance images. Methods: Image registration is employed to quantify the target’s 2D movement from its original position on a reference image. This 2D information is then combined with predictions from LightGBM models to determine the entire 3D displacement of the target. Additionally, the 2D displacement measurements are incorporated into the LightGBM models’ training set for continuous on-line re-optimization. The methods were evaluated using a curated dataset of real liver data; their performance in tracking and ability to offset system delays was analyzed. Results: On average, the image registration method yielded tracking errors of 1.19 mm. System delays of 200 ms, 400 ms and 600 ms led to tracking errors of 1.78 mm, 2.62 mm and 3.46 mm. The trained LightGBM models, once trained, reduced these errors by 29% to 46%. Conclusions: The challenges of incomplete data and system delay were effectively addressed by the investigated methods, which demonstrate great potential for real-time 3D tracking of various targets without needing prior knowledge of their displacement.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".