BoneSplit - A 3D Texture Painting Tool for Interactive Bone Separation in CT Images
Why this work is in the frame
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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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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 | 0.010 |
| 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