Modeling Prior Shape and Appearance Knowledge in Watershed Segmentat
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Bibliographic record
Abstract
Watershed transform is widely used in image segmentation. However, its shortcomings such as over-segmentation and sensitivity to noise often make it unsuitable as an automatic tool for segmenting medical images. Utilizing prior shape knowledge has been demonstrated to improve robustness of medical image segmentation algorithms. In this paper, we propose a novel method for incorporating prior shape and appearance knowledge into watershed segmentation. Our method is based on iteratively aligning a shape-histogram with the result of an improved k-means clustering algorithm. No human interaction is needed in the whole process. We demonstrate the robustness of our method through segmenting the corpora callosa from a set of 51 brain magnetic resonance (MR) images. Numerical validation of the results is provided.
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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 | 0.000 | 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) | 0.000 | 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