Prediction of free-breathing 4DCT lung deformation using probabilistic motion auto-encoders
Bibliographic record
Abstract
Radiotherapy treatment necessitates accurate tracking of the tumor in real-time, often during free-breathing. However, in lung cancer, the respiration entails a significant displacement of the tumor during radiation. This movement, if not well accounted for, can lead to an under-radiation of the tumor or damaging surrounding healthy regions. It is therefore paramount to be able to follow the displacement of the tumor over the entire respiratory cycle. In deep learning applications, it is important to have enough data to capture reliable and representative motion patterns. However, obtaining large amounts of dynamic images is known to be difficult, especially when there is a need to use manually annotated images. Consequently, even incomplete data are worth being utilized. In this work, we propose a model capable of predicting lungs deformations to predict missing phases in a 4D CT lungs dataset, based on probabilistic motion auto-encoders. The model uses the information from a reference 3D volume obtained at the beginning of treatment and a set of 2D surrogate images to predict the next 3D respiratory volumes. The proposed model was evaluated on a free-breathing 4DCT dataset of 165 patients treated for lung cancer. We achieve a mean performance of 81.70% structural similarity, a mean square error of 3.02% and a negative local cross correlation of 81.43% on a hold-out test set comprised of 34 patients. The proposed model can also be used to complete missing respiratory phases in datasets of 4DCT scans of lungs.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".