Fully Automated Fibroglandular Tissue Segmentation and Bias Correction in Breast MR Images Using Level Set Method
Bibliographic record
Abstract
In this paper an image processing method is proposed for bias correction and fibroglandular tissue segmentation from Magnetic Resonance Images (MRI) of human breast. The proposed method is based on level sets and includes three steps. In the pre-processing step, a chest wall line detection method is applied to separate the chest wall from the breast region in the MR images of breast. In the next step, a new level set algorithm is employed to estimate the bias field. The bias field estimation is used for intensity inhomogeneity correction, which leads to an efficient segmentation of the fibroglandular tissue. Finally, in the post-processing step, the skin layer is detected using morphological operations, and the fibroglandular tissue is extracted after skin layer subtraction. The proposed method has been validated on 2D images of an MR scan of the human breast. The implementation results show efficient performance of this method in tissue segmentation of MR images with the presence of intensity inhomogeneity.
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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) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".