SU‐FF‐I‐60: Delineation of Synovial Tissue in MR Images of the Knee for Patient Specific Dosimetry Planning of Radionuclide Synovectomy
Bibliographic record
Abstract
Purpose: Delineation of synovial tissue in MR images of the knee for feasibility in patient specific dosimetry planning of radionuclide synovectomy. Method and Materials: A semi automated method based on marker controlled watershed was developed to delineate the synovial tissue in MR images of the knee. Markers were calculated using gray reconstruction algorithm. Segmented regions were extracted interactively by using a graphical user interface. Images used for testing were T1‐weighted spin echo (TR 800, TE 20), post contrast enhanced, acquired using 1.5 Tesla MR Units (Siemens, Magnetron) and polarized knee coil. Image slices were of 0.625mm ×0.625mm in size and 3mm in thickness. Only one patient's data was used. Various performance metrics were determined for evaluating the method. Results: Results obtained from the semi automated method were compared with the manual results. Maximum overlap ratio of 86.3 % was achieved between the manually delineated and semi automatically delineated synovial tissue regions. Mean percentage error of 19% in the surface area of synovial tissue was recorded. Conclusion: This study indicates that developed method has more robustness and reproducibility as compared to the manual method. For future work this method can be applied to other patients data, pre contrast enhanced images for volume calculation of the synovial tissue and also to individualize radionuclide synovectomy treatments.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".