SU‐C‐BRA‐02: A Lung Tumor Autocontouring Algorithm Based On Particle Filter for Dynamic Magnetic Resonance
Bibliographic record
Abstract
Purpose: This study proposes a novel autocontouring algorithm for lung tumors based on particle filter. It is developed in the context of MR‐linac treatments and is validated on in‐vivo dynamic magnetic resonance images. Methods: A sequential Monte Carlo method called particle filter is combined with Otsu's thresholding technique to contour lung tumors on dynamic MR images. Four non‐small cell lung cancer (NSCLC) patients were imaged with a 1.5 T MR (MAGNETOM Aera, Siemens Healthcare, Erlangen, Germany) for one minute at a rate of 4 images per second. A pretreatment image‐processing step includes a manual contour of the tumor, the tumor's displacement, and its descriptive statistics. During treatment, the contours are automatically generated by thresholding the image in a window surrounding the center of mass of the particles. The Dice similarity coefficient (DSC), the precision, the recall, the Hausdorff distance (HD) and the difference in centroid positions (Δd) are calculated to compare with the expert's contours. Results: This autocontouring algorithm presents continuous adaptability and is independent of pre‐treatment training. The number of particles is proportional to the area of the tumor and increases the computational time at a rate of 2 ms for every 500 particles, whereas the contouring step adds a constant 14 ms. The contours’ comparison is obtained with a mean DSC of 0.89 to 0.91, mean precision of 0.88 to 0.92, mean recall of 0.88 to 0.95 and mean Δd of 0.6 to 2.3 mm. Conclusion: This work presents a new autocontouring algorithm for NSCLC patients on dynamic MR images. Combining the autocontours with a motion prediction algorithm presents a promising method for lung tumor online tracking treatments.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".