The accuracy and reproducibility of SPECT target volumes and activities estimated using an iterative adaptive thresholding technique
Bibliographic record
Abstract
OBJECTIVE: Our aim was to design a practical and reproducible image segmentation method for calculations of total absorbed doses in organs and tumours for internally delivered radioisotopes. We have built upon our previously proposed use of two separate thresholds and employed an iterative technique for semiautomatic selection of background regions for segmenting an object of interest using thresholds that depend on the source-to-background ratio of activity concentrations. METHODS: The parameters of curves relating volume and activity thresholds to source-to-background ratio were established using phantoms with 20 different inserts. The accuracy of our technique was validated using a second phantom experiment, whereas the reproducibility of volume, activity and dose estimates of organs and tumours was investigated using 13 patient studies. The accuracy and reproducibility of segmentations achieved were assessed using images reconstructed with three different methods that ranged from a standard clinical reconstruction to an advanced quantitative reconstruction approach. RESULTS: In the validation phantom experiment, bottle volumes and activities measured using iterative adaptive thresholding agreed on average with the true values to within 4%, regardless of the reconstruction method used. In the patient studies, volumes and activities estimated from the single-photon emission computed tomography images reconstructed with clinical software agreed with the volumes and activities estimated using the advanced reconstruction approach to within 6%, whereas the corresponding doses agreed to within 4%. CONCLUSION: The proposed iterative adaptive thresholding technique can accurately determine object volume and activity, which allows standard clinical reconstructions to generate absorbed dose estimates that are similar to those values obtained using more advanced reconstruction methods.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| 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".