TRU-IMP: techniques for reliable use of images in medical physics; a graphical user interface to analyze and compare segmentations in nuclear medicine
Bibliographic record
Abstract
Abstract Background. In the context of pharmacokinetic analyses, the segmentation method one uses has a large impact on the results obtained, thus the importance of transparency. Innovation. This paper introduces a graphical user interface (GUI), TRU-IMP, that analyzes time-activity curves and segmentations in dynamic nuclear medicine. This GUI fills a gap in the current technological tools available for the analysis of quantitative dynamic nuclear medicine image acquisitions. The GUI includes various techniques of segmentations, with possibilities to compute related uncertainties. Results. The GUI was tested on image acquisitions made on a dynamic nuclear medicine phantom. This allows the comparison of segmentations via their time-activity curves and the extracted pharmacokinetic parameters. Implications. The flexibility and user-friendliness allowed by the proposed interface make the analyses both easy to perform and adjustable to any specific case. This GUI permits researchers to better show and understand the reproducibility, precision, and accuracy of their work in quantitative dynamic nuclear medicine. Availability and Implementation. Source code freely available on GitHub: https://github.com/ArGilfea/TRU-IMP and location of the interface available from there. The GUI is fully compatible with iOS and Windows operating systems (not tested on Linux). A phantom acquisition is also available to test the GUI easily.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.047 | 0.021 |
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".