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 (R<sup>2</sup> = 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 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".