Three-dimensional spatial localization of thin fluorophore-filled capillaries in thick scattering media
Bibliographic record
Abstract
Fluorescence optical diffuse tomography (fDOT) is of much interest in molecular imaging to retrieve information from fluorescence signals emitted from specifically targeted bioprocesses deep within living tissues. An exciting application of fDOT is in the growing field of tissue engineering, where 3D non-invasive imaging techniques are required to ultimately grow 3D engineered tissues. Via appropriate labelling strategies and fluorescent probes, fDOT has the potential to monitor culture environment and cells viability non-destructively directly within the bioreactor environment where tissues are to be grown. Our ultimate objective is to image the formation of blood vessels in bioreactor conditions. Herein, we use a non-contact setup for small animal fDOT imaging designed for 3D light collection around the sample. We previously presented a time of flight approach using a numerical constant fraction discrimination technique to assign an early photons arrival time to every fluorescence time point-spread function collected around the sample. Towards bioreactor in-situ imaging, we have shown the capability of our approach to localize a fluorophore-filled 500 μm capillary immersed coaxially in a cylindrically shaped bioreactor phantom containing an absorbing/scattering medium representative of experiments on real tissue cultures. Here, we go one step further, and present results for the 3D localization of thinner indocyanine green labelled capillaries (250 μm and 360 μm inner diameter) immersed in the same phantom conditions and geometry but with different spatial configurations (10° and 30° capillary inclination).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".