Automatic atlas‐based segmentation of the breast in MRI for 3D breast volume computation
Bibliographic record
Abstract
PURPOSE: Breast density is considered a significant risk factor and an important biomarker influencing the later risk of breast cancer. Prior breast segmentation is required when quantifying breast density with MRI in order to calculate the total breast volume and exclude nonbreast surrounding tissues. This paper describes an automatic 3D breast volume segmentation approach. METHODS: The method is based on 3D local edge detection using phase congruency and Poisson surface reconstruction to extract the total breast volume. The boundary localization framework is integrated to a subsequent shape atlas-based segmentation using a Laplacian framework. RESULTS: The 3D segmentation achieves breast-air and breast-chest wall boundary localization errors with a median of 1.36 mm and 2.68 mm, respectively, and an average volume error of 153.8 cm(3) when tested on 409 MRI datasets. Furthermore, the breast volume assessment technique will produce a 5.3% variability in the estimation of breast density in the tested population. CONCLUSIONS: The fully automated segmentation approach of the breast in MRI allows the computation of total breast volume, a step required for breast density assessment. The use of features invariant to image intensity and a shape atlas to reinforce shape consistency are attractive characteristics of the method. Error analysis demonstrates that 5.3% variability in the estimation of breast density incurred by the method is an acceptable trade-off.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.002 | 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".