Preliminary assessment of a convolutional neural network for localization of a radioactive source with a hand-held gamma detector
Bibliographic record
Abstract
Intraoperative localization of non-palpable lesions is an essential task in cancer surgery, and radioguided surgery using a gamma probe is a commonly used technique for this purpose. While gamma probes have proven to be effective for precise detection of lower energy radionuclides, scattering and septal penetration of the collimator at higher energies rapidly degrades the probe’s resolution. To combat these challenges, we aim to investigate the accuracy of a neural network in predicting the location of a high-energy radioactive source. In this study, we use Monte Carlo simulations to model a gamma probe containing a multifocal collimator and 4-segmented scintillation crystal. A 511 keV radioactive point source was positioned at various x, y locations 35 mm below the probe and a 4-channel energy spectrum was recorded for 300 simulations. A convolutional neural network (CNN) featuring three blocks of 1D-convolutions was used to predict the x, y source location from the energy spectra. The CNN hyperparameters were tuned using cross validation, and 20% of the data was reserved for testing. The results demonstrated a strong linear relationship (R2 = 0.92) between the true and predicted location. Achieving a mean radial error of 2.9 mm (±1.8 mm standard deviation) enabled the location of the radioactive source to be predicted to within 6.5 mm 95% of the time, a significant improvement compared to existing gamma probes where the resolution can be tens of millimeters. Overall, this work presents a new technique to improve the localization of positron-emitting radiolabels, thereby enhancing surgical accuracy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".