A Novel Rotationally Invariant Region-Based Hidden Markov Model for Efficient 3-D Image Segmentation
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Bibliographic record
Abstract
We present a novel 3-D region-based hidden Markov model (rbHMM) for efficient unsupervised 3-D image segmentation. Our contribution is twofold. First, rbHMM employs a more efficient representation of the image data than current state-of-the-art HMM-based approaches that are based on either voxels or rectangular lattices/grids, thus resulting in a faster optimization process. Second, our proposed novel tree-structured parameter estimation algorithm for the rbHMM provides a locally optimal data labeling that is invariant to object rotation, which is a highly valuable property in segmentation tasks, especially in medical imaging where the segmentation results need to be independent of patient positioning in scanners in order to minimize methodological variability in data analysis. We demonstrate the advantages of our proposed technique over grid-based HMMs by validating on synthetic images of geometric shapes as well as both simulated and clinical brain MRI scans. For the geometric shapes data, our method produced consistently accurate segmentation results that were also invariant to object rotation. For the brain MRI data, our white matter and gray matter segmentation resulted in substantially higher robustness and accuracy levels with improved Dice similarity indices of 4.60% (p=0.0022) and 7.71% , respectively.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it