A novel fully automatic measurement of apparent breast volume from trunk surface mesh
Bibliographic record
Abstract
This paper presents a novel method for assessing apparent breast volume from trunk surface mesh without any manual intervention. The proposed method requires a closed and smooth triangular mesh of the trunk. It comprises four main steps: automatic nipple localization, automatic breasts delineation, chest-wall interpolation and volume computation. The mean curvature is computed for each vertex using a quadratic fitting approach and used as an indicator to determine the convex fold of the breasts. The delineation is modeled as an ellipse in the frontal plane and all the vertices inside it are removed. The remaining ones are used to interpolate the chest wall with radial basis functions. The voxels inside the resulting mesh without breasts are then subtracted from the original voxelized volume to generate the breasts volume. The validation is conducted on 30 adolescent female for each of which an MRI and a trunk surface (TS) acquisitions were available. Three breast volumes are considered: the anatomical volumes (AV) manually segmented on the MRI, the external volumes computed with the proposed method first in prone position (EVP) using the trunk mesh extracted from the MRI, and second, in standing position (EVS) using the TS's mesh. Significant correlations (R> 0.77) are found between each two of the three volumes. AVs are much larger than both EVS and EPS. In fact, the manual segmentation using MRI slices allows for a direct visualization of the breast posterior delineation. Computed automatically, EVS and EPS are highly similar, indicating that the proposed method is robust to changes from prone to standing position. No significant difference between the regressions on the left and right breasts is noted. Fully-automatic 3D breast volumetry from trunk surface mesh is feasible and provides measurements that are highly correlated to manual MRI volumetry and robust to changes in posture.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".