Performance of an automated segmentation algorithm for 3D MR renography
Why this work is in the frame
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Bibliographic record
Abstract
The accuracy and precision of an automated graph-cuts (GC) segmentation technique for dynamic contrast-enhanced (DCE) 3D MR renography (MRR) was analyzed using 18 simulated and 22 clinical datasets. For clinical data, the error was 7.2 +/- 6.1 cm(3) for the cortex and 6.5 +/- 4.6 cm(3) for the medulla. The precision of segmentation was 7.1 +/- 4.2 cm(3) for the cortex and 7.2 +/- 2.4 cm(3) for the medulla. Compartmental modeling of kidney function in 22 kidneys yielded a renal plasma flow (RPF) error of 7.5% +/- 4.5% and single-kidney GFR error of 13.5% +/- 8.8%. The precision was 9.7% +/- 6.4% for RPF and 14.8% +/- 11.9% for GFR. It took 21 min to segment one kidney using GC, compared to 2.5 hr for manual segmentation. The accuracy and precision in RPF and GFR appear acceptable for clinical use. With expedited image processing, DCE 3D MRR has the potential to expand our knowledge of renal function in individual kidneys and to help diagnose renal insufficiency in a safe and noninvasive manner.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it