Delineating the inner bladder surface using uniform contractions from the outer surface under variable bladder filling conditions
Bibliographic record
Abstract
OBJECTIVE: To evaluate the methods to delineate the inner bladder (IB) surface using a uniform contraction from the outer bladder (OB) surface, assuming the bladder wall (BW) is either of constant thickness, constant volume or variable volume. METHODS: 14 prostate intensity-modulated radiotherapy patients with 2 planning CTs were identified. For both CTs, OB was delineated using model-based segmentation. IB was delineated manually. Then, using uniform contractions from OB, the position of IB was approximated using a: 2.5-mm contraction, patient-specific contraction, patient-specific constant wall volume method and variable wall volume method. The structures created using those strategies were compared against the manual IB contours using geometric and dosimetric indices. RESULTS: In the presence of variable bladder filling, use of a generic or patient-specific constant contraction resulted in a significant overestimation of IB volume (+12 and +13 cm(3), respectively; p < 0.001) that was inversely correlated with the difference in urine volume between the scans (R(2) > 0.86). Mean differences across 95% of IB surfaces were ≤2 mm for methods using either constant or variable wall volume. Mean dose-volume histogram (DVH) differences were <1 cm(3) across the whole BW DVH when using the method that assumed a variable wall volume. CONCLUSION: The variable volume BW model provided the best approximation of the IB surface position under varying filling conditions, based on geometric and dosimetric indices. ADVANCES IN KNOWLEDGE: Use of the equation derived in this research provides a quick and accurate method to delineate the hollow BW on serial imaging for the purposes of dose reconstruction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".