Tensor dissimilarity based adaptive seeding algorithm for DT-MRI visualization with streamtubes
Bibliographic record
Abstract
In this paper, we propose an adaptive seeding strategy for visualization of diffusion tensor magnetic resonance imaging (DT-MRI) data using streamtubes. DT-MRI is a medical imaging modality that captures unique water diffusion properties and fiber orientation information of the imaged tissues. Visualizing DT-MRI data using streamtubes has the advantage that not only the anisotropic nature of the diffusion is visualized but also the underlying anatomy of biological structures is revealed. This makes streamtubes significant for the analysis of fibrous tissues in medical images. In order to avoid rendering multiple similar streamtubes, an adaptive seeding strategy is employed which takes into account similarity of tensors in a given region. The goal is to automate the process of generating seed points such that regions with dissimilar tensors are assigned more seed points compared to regions with similar tensors. The algorithm is based on tensor dissimilarity metrics that take into account both diffusion magnitudes and directions to optimize the seeding positions and density of streamtubes in order to reduce the visual clutter. Two recent advances in tensor calculus and tensor dissimilarity metrics are utilized: the Log-Euclidean and the J-divergence. Results show that adaptive seeding not only helps to cull unnecessary streamtubes that would obscure visualization but also do so without having to compute the culled streamtubes, which makes the visualization process faster.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".