Second order DTMR image segmentation using random walker
Bibliographic record
Abstract
Image segmentation is a method of separating an image into regions of interest, such as separating an object from the background. The random walker image segmentation technique has been applied extensively to scalar images and has demonstrated robust results. In this paper we propose a novel method to apply the random walker method to segmenting non-scalar diffusion tensor magnetic resonance imaging (DT-MRI) data. Moreover, we used a non-parametric probability density model to provide estimates of the regional distributions enabling the random walker method to successfully segment disconnected objects. Our approach utilizes all the information provided by the tensors by using suitable dissimilarity tensor distance metrics. The method uses hard constraints for the segmentation provided interactively by the user, such that certain tensors are labeled as object or background. Then, a graph structure is created with the tensors representing the nodes and edge weights computed using the dissimilarity tensor distance metrics. The distance metrics used are the Log-Euclidean and the J-divergence. The results of the segmentations using these two different dissimilarity metrics are compared and evaluated. Applying the approach to both synthetic and real DT-MRI data yields segmentations that are both robust and qualitatively accurate.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".