Lung tumor boundary tracking in MRI with moving mesh correspondences for adaptive radio therapy
Bibliographic record
Abstract
Delineation of lung tumor regions from magnetic resonance imaging (MRI) poses many difficulties due to MR signal similarities of the region of interest and surrounding area as well as the influence of respiration. However, accurate segmentation of the tumor region is of utmost importance in planning a radiation therapy since a small error can result in some healthy tissues to receive excessive radiation. This study presents a semi-automated method to delineate lung tumor regions from a sequence of MRIs. The proposed method uses a non-rigid image registration framework to propagate the boundaries of the tumor region in MRI acquired during a radiation treatment stage, given manual segmentation on frames acquired during pretreatment stage. We investigate two approaches: 1) the first one utilizes manual segmentation of the first frame during the pretreatment stage; and 2) the second one utilizes manual segmentation of all the frames during the pretreatment stage. We evaluated the proposed approaches over a sequence of 400 images acquired from 4 patients. The proposed method based on the utilization of all the frames yielded a Dice score of 0.90 ± 0.04 and a Hausdorff distance of 1.17 ± 0.35 pixels (2.83 ± 0.79 mm) in comparison to expert manual segmentation.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".