Technology assessment of automated atlas based segmentation in prostate bed contouring
Bibliographic record
Abstract
BACKGROUND: Prostate bed (PB) contouring is time consuming and associated with inter-observer variability. We evaluated an automated atlas-based segmentation (AABS) engine in its potential to reduce contouring time and inter-observer variability. METHODS: An atlas builder (AB) manually contoured the prostate bed, rectum, left femoral head (LFH), right femoral head (RFH), bladder, and penile bulb of 75 post-prostatectomy cases to create an atlas according to the recent RTOG guidelines. 5 other Radiation Oncologists (RO) and the AABS contoured 5 new cases. A STAPLE contour for each of the 5 patients was generated. All contours were anonymized and sent back to the 5 RO to be edited as clinically necessary. All contouring times were recorded. The dice similarity coefficient (DSC) was used to evaluate the unedited- and edited- AABS and inter-observer variability among the RO. Descriptive statistics, paired t-tests and a Pearson correlation were performed. ANOVA analysis using logit transformations of DSC values was calculated to assess inter-observer variability. RESULTS: The mean time for manual contours and AABS was 17.5- and 14.1 minutes respectively (p = 0.003). The DSC results (mean, SD) for the comparison of the unedited-AABS versus STAPLE contours for the PB (0.48, 0.17), bladder (0.67, 0.19), LFH (0.92, 0.01), RFH (0.92, 0.01), penile bulb (0.33, 0.25) and rectum (0.59, 0.11). The DSC results (mean, SD) for the comparison of the edited-AABS versus STAPLE contours for the PB (0.67, 0.19), bladder (0.88, 0.13), LFH (0.93, 0.01), RFH (0.92, 0.01), penile bulb (0.54, 0.21) and rectum (0.78, 0.12). The DSC results (mean, SD) for the comparison of the edited-AABS versus the expert panel for the PB (0.47, 0.16), bladder (0.67, 0.18), LFH (0.83, 0.18), RFH (0.83, 0.17), penile bulb (0.31, 0.23) and rectum (0.58, 0.09). The DSC results (mean, SD) for the comparison of the STAPLE contours and the 5 RO are PB (0.78, 0.15), bladder (0.96, 0.02), left femoral head (0.87, 0.19), right femoral head (0.87, 0.19), penile bulb (0.70, 0.17) and the rectum (0.89, 0.06). The ANOVA analysis suggests inter-observer variability among at least one of the 5 RO (p value = 0.002). CONCLUSION: The AABS tool results in a time savings, and when used to generate auto-contours for the femoral heads, bladder and rectum had superior to good spatial overlap. However, the generated auto-contours for the prostate bed and penile bulb need improvement.
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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 | 0.013 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".