Scribble-based weakly supervised method for segmentation of neonatal cerebral ventricles from 3D ultrasound images
Bibliographic record
Abstract
Compared to conventional two-dimensional (2D) ultrasound, three-dimensional (3D) ultrasound (US) images are a more sensitive alternative for monitoring the size and shape of neonatal cerebral lateral ventricles for monitoring intraventricular hemorrhaging (IVH). The ventricles must be segmented by an expert to estimate the ventricular volume, which can be time-consuming and difficult to obtain. In this paper, we describe a scribble-based weakly supervised segmentation method that trains only on non-expert drawn scribbles. We trained and tested two segmentation methods, a vanilla 3D U-Net benchmark and a weakly supervised learning for medical image segmentation (WSL4MIS) method built into the 3D U-Net model, using 56 3D US images. We performed two experiments, the first where models were trained, validated, and tested on 25/5/15 images respectively, and a second where models were trained, validated, and tested on 36/5/15 images respectively. For the first experiment, the 3D U-Net and WSL4MIS achieved a mean ± standard deviation and Dice similarity coefficients (DSC) of 40.4±9.4% and 42.8±7.9%, respectively. The second experiment yielded a DSC of 40.4±7.2% and 44.8±9.5% for 3D U-Net and WSL4MIS, respectively. For both experiments, the WSL4MIS method had a higher mean DSC and lower standard deviation than the baseline 3D U-Net when both used scribbles for training. When trained on 36 images and scribbles, the WSL4MIS achieved statistically significant DSC values compared to the 3D U-Net model based on the Wilcoxon signed-rank test.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 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 | 0.001 |
| 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".