SCU-Net: A deep learning method for segmentation and quantification of breast arterial calcifications on mammograms
Bibliographic record
Abstract
Abstract Purpose Measurements of breast arterial calcifications (BAC) can offer a personalized, noninvasive approach to risk-stratify women for cardiovascular disease such as heart attack and stroke. We aim to detect and segment breast arterial calcifications in mammograms accurately and suggest novel measurements to quantify detected BAC for future clinical applications. Methods To separate BAC in mammograms, we propose a light-weight fine vessel segmentation method Simple Context U-Net (SCU-Net). Due to the large image size of mammograms, we adopt a patch-based way to train SCU-Net and obtain the final whole-image-size results by stitching patch-wise results together. To further quantify calcifications, we test five quantitative metrics to inspect the progression of BAC for subjects: Sum of Mask Probability Metric (𝒫ℳ), Sum of Mask Area Metric (𝒜ℳ), Sum of Mask Intensity Metric (𝒮ℐℳ), Sum of Mask Area with Threshold Intensity Metric (𝒯𝒜ℳ X ) and Sum of Mask Intensity with Threshold X Metric (𝒯 𝒮ℐℳ X ). Finally, we demonstrate the ability of the metrics to longitudinally measure calcifications in a group of 26 subjects and evaluate our quantification metrics compared to calcified voxels and calcium mass on breast CT for 10 subjects. Results Our segmentation results are compared with state-of-the-art network architectures based on recall, precision, accuracy, F1-score/Dice Score and Jaccard Index evaluation metrics and achieve corresponding values of 0.789, 0.708, 0.997, 0.729, and 0.581 for whole-image-size results. The quantification results all show > 95% correlation between quantification measures on predicted masks of SCU-Net as compared to the groundtruth and measurement of calcification on breast CT. For the calcifications quantification measurement, our calcification volume (voxels) results yield R 2 -correlation values of 0.834, 0.843, 0.832, 0.798, and 0.800 for the 𝒫ℳ, 𝒜ℳ, 𝒮ℐℳ, 𝒯𝒜ℳ 100 , 𝒯 𝒮ℐℳ 100 metrics, respectively; our calcium mass results yield comparable R 2 -correlation values of 0.866, 0.873, 0.840, 0.774, and 0.798 for the same metrics. Conclusions SCU-Net is a simple method to accurately segment arterial calcification retrospectively on routine mammograms. Quantification of the calcifications based on this segmentation in the retrospective cohort study has sufficient sensitivity to detect the normal progression over time and should be useful for future research and clinical applications.
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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 | 0.000 | 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".