3D Semantic Segmentation of Airway Abnormalities on UTE-MRI with Reinforcement Learning on Deep Supervision
Bibliographic record
Abstract
Cystic fibrosis (CF) monitoring traditionally relies on CT scans, which involve radiation exposure concerns. Ultra-short echo time (UTE) MRI has emerged as a promising radiation-free alternative for lung imaging. However, automated segmentation of CF lesions on UTE MRI has not been reported yet, mainly due to lower signal-to-noise ratio and contrast compared to CT. This study evaluates the feasibility of fully automated semantic segmentation of three main hallmarks of CF: bronchiectasis, bronchial wall thickening, and bronchial mucus. To address the challenges of low proton MRI signal and resolution, we propose a novel Reinforcement learning for deep Supervision adapted to nnU-Net (RiSeNet). This approach enhances the standard nnU-Net by dynamically adjusting deep supervision weights during training through reinforcement learning. We compare RiSeNet against both the baseline nnU-Net and selected state-of-the-art architectures: SAMed and nnSAM for global context modeling, MedNeXt for large-scale feature capture, and U-Mamba for efficient volumetric processing. All models were evaluated using registered same-day CT-derived ground truth labels. Results demonstrate RiSeNet's superior performance in both accuracy and efficiency when handling the unique challenges of UTE MRI 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 | 0.002 |
| 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 | 0.001 |
| 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 | 0.000 |
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".