Bibliographic record
Abstract
In this paper we extend two popular classical scalar medical image segmentation techniques to diffusion tensor magnetic resonance images (DTMRI). We propose DT-snakes and DT-livewire through modifying the external image forces in snakes and cost terms in livewire. The new forces and cost terms are derived from and operate on a DT field rather than a scalar image. This is achieved by making use of recent advances in DT calculus and DT dissimilarity measures, as well as DT smoothing and DT interpolation. Proper quantification of tensor dissimilarity allows for defining spatial gradient vectors and gradient magnitudes of DT fields, an essential component for attracting snakes or livewire to target boundaries in DT images. DT calculus enables weighted averaging of tensors which is essential for both pre-smoothing of DT images prior to segmentation, as well as interpolation of tensors on non-grid positions in the image. We evaluate different recent DT tensor dissimilarity metrics including the Log-Euclidean and the square root of the J-divergence. We present qualitative and quantitative DT segmentation results on both synthetic and real cardiac and brain DTMRI data
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".