MicroNucML: A machine learning approach for micronuclei segmentation and the refinement of nuclei-micronuclei relationships
Bibliographic record
Abstract
Abstract Micronuclei (MN) are structures containing small fragments of DNA, arising from mitotic errors or failed DNA repair attempts. Therefore, MN serve as markers of genomic instability and are typically quantified either manually or through threshold-based methods, which can be tedious and inaccurate, leading to varying degrees of success and throughput. By employing a two-phase labeling approach that utilizes polygon and brush segmentation, along with refinement using SAM2, we developed a high-quality MN segmentation tool. Subsequent data augmentation, which captured heterogeneity in image quality and color diversity, enabled us to train a generalizable Mask-RCNN model optimized for small object detection, achieving state-of-the-art performance in MN detection. Finally, we applied our model to immunofluorescence data obtained from cell lines exposed to DNA damage conditions to gain biological insights into MN dynamics and their role in inducing genome instability. In summary, this work establishes an accessible resource for systematically studying genome instability with significantly greater fidelity and sensitivity, enabling insights into damage biology that were previously unresolved.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".