Surface reconstruction from structured-light images for radiation therapy
Bibliographic record
Abstract
To design and deliver proper radiation treatment for cancer patients, knowledge of the body's surface in the affected area is required. Currently, surface information is obtained by using a manually operated tracer. The drawbacks of this contact method include slow operation, and errors in repositioning the patient in an x-ray machine. Utilization of MRI or CT is also possible but expensive. We propose a non-contact, quick, inexpensive method to reconstruct the surface. In our non-contact method, a mask with transparent circular coloured spots and a black background, and an incoherent light source are used to create structured-light images. Colour coding is necessary to establish the correspondence between the projected and the observed patterns, which is essential for surface reconstruction. The deformed light pattern is photographed by an offset camera and analyzed. First, noise reduction is performed because images are noisy due to the low-light conditions and low sensitivity of an off-the-shelf camera. Then, pattern elements (light elliptical spots) are found in the image. We use an inverse polynomial to model the intensity of a light spot, which results in a non-convex, least-squares optimization problem. Next, spots are assigned to a grid according to their colours and location, and errors are corrected using the relative position of the spots. Finally, spatial coordinates of the surface points are computed and surface reconstruction is performed. The described algorithms are implemented as a MATLAB package, which converts the acquired images into a three-dimensional surface. The developed system is inexpensive, and it can easily be mounted on an x-ray machine. The software package can run on any standard PC.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".