3D live-wire-based semi-automatic segmentation of medical images
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Segmenting anatomical structures from medical images is usually one of the most important initial steps in many applications, including visualization, computer-aided diagnosis, and morphometric analysis. Manual 2D segmentation suffers from operator variability and is tedious and time-consuming. These disadvantages are accentuated in 3D applications and, the additional requirement of producing intuitive displays to integrate 3D information for the user, makes manual segmentation even less approachable in 3D. Robust, automatic medical image segmentation in 2D to 3D remains an open problem caused particularly by sensitivity to low-level parameters of segmentation algorithms. Semi-automatic techniques present possible balanced solution where automation focuses on low-level computing-intensive tasks that can be hidden from the user, while manual inter- vention captures high-level expert knowledge nontrivial to capture algorithmically. In this paper we present a 3D extension to the 2D semi-automatic live-wire technique. Live-wire based contours generated semi-automatically on a selected set of slices are used as seed points on new unseen slices in different orientations. The seed points are calculated from intersections of user-based live-wire techniques with new slices. Our algorithm includes a step for ordering the live-wire seed points in the new slices, which is essential for subsequent multi-stage optimal path calculation. We present results of automatically detecting contours in new slices in 3D volumes from a variety of medical images.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Open science | 0.002 | 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