Fluorescence depth estimation from wide-field optical imaging data for guiding brain tumor resection: a multi-inclusion phantom study
Bibliographic record
Abstract
Studies have shown that fluorescent agents demarcate tumor from surrounding brain tissue and offer intraoperative guidance during resection. However, visualization of fluorescence signal from tumor below the surgical surface or through the appearance of blood in the surgical field is challenging. We have previously described red light imaging techniques for estimating fluorescent depths in turbid media. In this study, we evaluate these methods over a broader range of fluorophore concentrations, and investigate the ability to resolve multiple fluorescent emissions in the same plane or at different depths along the axis of imaging. A tungsten halogen lamp is used as a broadband white light source for reflectance imaging. Fluorescence from Alexa Fluor 647 is excited with a 635 nm diode laser. Reflectance and fluorescence spectral data are gathered between 670 and 720 nm with the use of a liquid crystal tunable filter and recorded on a sCMOS camera. Results show that two fluorescent emissions can be resolved within 2 mm if they are in the same plane or within 3 mm if they are at different depths along the axis of imaging up to 6 mm below the surface.
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How this classification was reachedexpand
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".