BoneSplit - A 3D Texture Painting Tool for Interactive Bone Separation in CT Images
Bibliographic record
Abstract
We present an efficient interactive tool for separating collectively segmented bones and bone fragments in 3D\ncomputed tomography (CT) images. The tool, which is primarily intended for virtual cranio-maxillofacial (CMF)\nsurgery planning, combines direct volume rendering with an interactive 3D texture painting interface to enable\nquick identification and marking of individual bone structures. The user can paint markers (seeds) directly on\nthe rendered bone surfaces as well as on individual CT slices. Separation of the marked bones is then achieved\nthrough the random walks segmentation algorithm, which is applied on a graph constructed from the collective\nbone segmentation. The segmentation runs on the GPU and can achieve close to real-time update rates for volumes\nas large as 5123. Segmentation editing can be performed both in the random walks segmentation stage and in a\nseparate post-processing stage using a local 3D editing tool. In a preliminary evaluation of the tool, we demonstrate\nthat segmentation results comparable with manual segmentations can be obtained within a few minutes.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
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".