Sliding slice: A novel approach for high accuracy and automatic 3D localization of seeds from CT scans
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
We present a conceptually novel principle for 3D reconstruction of prostate seed implants. Unlike existing methods for implant reconstruction, the proposed algorithm uses raw CT data (sinograms) instead of reconstructed CT slices. Using raw CT data solves several inevitable problems related to the reconstruction from CT slices. First, the sinograms are not affected by reconstruction artifacts in the presence of metallic objects and seeds in the patient body. Second, the scanning axis is not undersampled as in the case of CT slices; as a matter of fact the scanning axis is the most densely sampled and each seed is typically represented by several hundred samples. Moreover, the shape of a single seed in a sinogram can be modeled exactly, thus facilitating the detection. All this allows very accurate 3D reconstruction of both position and the orientation of the seeds. Preliminary results indicate that the seed position can be estimated with 0.15 mm accuracy (average), while the orientation estimate accuracy is within 3 deg on average. Although the main contribution of the paper is to present a new principle of reconstruction, a preliminary implementation is also presented as a proof of concept. The implemented algorithm has been tested on a phantom and the obtained results are presented to validate the proposed approach.
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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