MO‐AB‐BRA‐09: Temporally Realistic Manipulation a 4D Biomechanical Lung Phantom for Evaluation of Simultaneous Registration and Segmentation
Bibliographic record
Abstract
Purpose: To produce multi‐modality compatible, realistic datasets for the joint evaluation of segmentation and registration with a reliable ground truth using a 4D biomechanical lung phantom. The further development of a computer controlled air flow system for recreation of real patient breathing patterns is incorporated for additional evaluation of motion prediction algorithms. Methods: A pair of preserved porcine lungs was pneumatically manipulated using an in‐house computer controlled respirator. The respirator consisted of a set of bellows actuated by a 186 W computer controlled industrial motor. Patient breathing traces were recorded using a respiratory bellows belt during CT simulation and input into a control program incorporating a proportional‐integral‐derivative (PID) feedback controller in LabVIEW. Mock tumors were created using dual compartment vacuum sealed sea sponges. 65% iohexol,a gadolinium‐based contrast agent and 18F‐FDG were used to produce contrast and thus determine a segmentation ground truth. The intensity distributions of the compartments were then digitally matched for the final dataset. A bifurcation tracking pipeline provided a registration ground truth using the bronchi of the lung. The lungs were scanned using a GE Discovery‐ST PET/CT scanner and a Phillips Panorama 0.23T MRI using a T1 weighted 3D fast field echo (FFE) protocol. Results: The standard deviation of the error between the patient breathing trace and the encoder feedback from the respirator was found to be ±4.2%. Bifurcation tracking error using CT (0.97×0.97×3.27 mm3 resolution) was found to be sub‐voxel up to 7.8 cm displacement for human lungs and less than 1.32 voxel widths in any axis up to 2.3 cm for the porcine lungs. Conclusion: An MRI/PET/CT compatible anatomically and temporally realistic swine lung phantom was developed for the evaluation of simultaneous registration and segmentation algorithms. With the addition of custom software and mock tumors, the entire package offers ground truths for benchmarking performance with high fidelity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".