Dbahnet: Dual-Branch Attention-Based Hybrid Network for High-Resolution 3d Micro-Ct Bone Scan Segmentation
Bibliographic record
Abstract
The precise segmentation of cortical and trabecular bone compartments in high-resolution micro-computed tomography (µCT) scans is crucial for evaluating bone structure and understanding how different medical treatments and mechanical loadings affect bone morphology, offering valuable insights into osteoporosis. In this work, we propose a novel hybrid neural network architecture named Dual-Branch Attention-based Hybrid Network (DBAHNet) for 3D µCT segmentation. DBAHNet combines both transformers and convolution neural networks in a dual-branch fashion and fuses their respective information at each hierarchical level, to better capture long-range dependencies and local features, and for a better understanding of the contextual representation. We train and evaluate DBAHNet on three datasets of high-resolution (<5µm) µCT scans of mouse tibiae. The results show that the proposed DBAHNet achieves state-of-the-art performance by surpassing several popular architectures. Our model also achieves a precise segmentation of the cortical and trabecular bone compartments along different regions of the bone, demonstrating a comprehensive understanding of the bone. Models and code are available at GitHub.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".