Fluorescence depth estimation in pre-clinical oral cancer models using spatial frequency domain imaging
Bibliographic record
Abstract
Clinical trials with novel fluorescence contrast agents for head and neck cancer are driving new applications for fluorescence-guided surgery. Two-dimensional fluorescence imaging systems, however, provide limited in vivo assessment capabilities to determine tumor invasion depth below the mucosal surface. Here, we investigate the use of spatial frequency domain imaging (SFDI) methods for sub-surface fluorescence in tissue-simulating oral cancer phantoms. A two-step profile-correction approach for SFDI is under development to account for the complex surface topography of the oral cavity. First, for structured-illumination estimation of the surface profile, we are evaluating gray code and phase shift profilometry methods in agar-based oral cavity phantoms to maximize resolution and minimize sensitivity to surface discontinuities. Second, for profile-correction of the diffuse reflectance, global lighting effects within the oral cavity – analogous to an integrating sphere – are modeled using a multi-bounce numerical model. Subsurface fluorescence imaging is enabled based on the variations in optical sampling depth that result from changes in spatial frequency. An analytical depth recovery approach is based on a numerical diffusion theory model for semi-infinite fluorescence slabs of variable thickness. Depth estimation is evaluated in an agar-based phantom with fluorescence inclusions of thicknesses 1-5.5 mm originating from the top surface (“iceberg model”). Future clinical studies are necessary to assess in vivo performance and intraoperative workflow.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".