Unsupervised Domain Adaptation of MRI Skull-stripping Trained on Adult Data to Newborns
Bibliographic record
Abstract
Skull-stripping is an important first step when analyzing brain Magnetic Resonance Imaging (MRI) data. Deep learning-based supervised segmentation models, such as the U-net model, have shown promising results in automating this segmentation task. However, when it comes to newborn MRI data, there are no publicly available brain MRI datasets that come with manually annotated segmentation masks to be used as labels during the training of these models. Manual segmentation of brain MR images is time-consuming, labor-intensive, and requires expertise. Furthermore, using a segmentation model trained on adult brain MR images for segmenting newborn brain images is not effective due to a large domain shift between adult and newborn data. As a result, there is a need for more efficient and accurate skull-stripping methods for newborns’ brain MRIs. In this paper, we present an unsupervised approach to adapt a U-net skull-stripping model trained on adult MRI to work effectively on newborns. Our results demonstrate the effectiveness of our novel unsupervised approach in enhancing segmentation accuracy. Our proposed method achieved an overall Dice coefficient of 0.916 ± 0.032 (mean ± std), and our ablation studies confirmed the effectiveness of our proposal. Remarkably, despite being unsupervised, our model’s performance stands in close proximity to that of the current state-of-the-art supervised models against which we conducted our comparisons. These findings indicate the potential of this method as a valuable, easier, and faster tool for supporting healthcare professionals in the examination of MR images of newborn brains. All the codes are available at: https://github.com/abbasomidi77/DAUnet.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".