Segmentation of Mouse Brain Slices with Unsupervised Domain Adaptation Considering Cross-sectional Locations
Bibliographic record
Abstract
Images of mouse brain slices, obtained under slightly different experimental conditions, are available in 84 datasets in the NeuroGT database (https://ssbd.riken.jp/neurogt/). Our goal was to obtain semantic segmentation results for eight brain anatomical regions. However, out of 84 datasets, only one dataset had true labels that could be used to train a convolutional neural network (CNN), and it was incomplete (131 out of 162 images). A segmentation model trained with the labeled images was less accurate on other images obtained under different experimental conditions because of differences of the image properties. We therefore tried Unsupervised Domain Adaptation (UDA), wherein the parameters of the CNN trained on the labeled images (source) were transferred to the unlabeled images (target). We used the positional information of the sample slices associated with each image to propose a novel loss function that approximated the class occurrence probabilities of segmentation results obtained from source and target images of brain samples at similar sliced locations, and we introduced it into the UDA. The proposed UDA method achieved an mIoU of 78.34%, which was 8% more accurate than the previous UDA methods such as Contrastive Learning and Self-Training (CLST) and Maximum Classifier Discrepancy (MCD). We demonstrated experimentally that the proposed method was useful for segmenting biomedical images with a small amount of incomplete training data.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".