A 3D segmentation framework for an accurate extraction of the spongy and cortical bones from the MRI data
Bibliographic record
Abstract
In this paper we proposed a new framework for obtaining the spongy and cortical bones from the MRI data. The method focuses on the accurate extraction of the edges of the target tissues, which is the main drawback of the previous works. This framework first limits the searching area for the bone voxels from the whole data to a small strip around the edges of the cortical and spongy bones then applies a very accurate segmentation on the searching area using the newly developed deformable kernel Fuzzy C-Means (DKFCM) algorithm, which is proposed in this paper. Comparing the results of this work with previous segmentation methods on a testing dataset consisting of 10,485,760 voxels demonstrates the superiority of the proposed method especially on the edges of the spongy and cortical bone.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".