A Semi-Supervised Framework for Cardiac MRI Segmentation via Multi-Constraint Collaborative Self-Training
Bibliographic record
Abstract
To address the challenges of limited annotated data and the adverse impact of noisy pseudolabels on model generalization in cardiac magnetic resonance imaging (MRI) segmentation, a novel semi-supervised segmentation framework based on multi-constraint collaborative self-training was developed.The proposed approach integrates uncertainty-guided pseudolabel quality control, prototype-driven inter-and intra-class consistency constraints, and a multi-scale adversarial learning mechanism.During each iteration of self-training, a dynamic selection of pseudo-labels generated by unlabeled samples was conducted using a method that fuses confidence and entropy-based uncertainty quantification, thereby enhancing the reliability of the pseudo-supervision signals.Simultaneously, class-specific prototype vectors were dynamically maintained to enforce explicit constraints that encourage intra-class feature aggregation and inter-class feature separability, improving the discriminative capacity of the feature space.In addition, both global and local discriminators were introduced to impose dual-level quality constraints on the global morphology and local structural details of the segmentation outputs, resulting in refined boundary delineation and enhanced structural consistency.Extensive experiments conducted on the publicly available ACDC dataset demonstrated that the proposed method achieved an average Dice Similarity Coefficient (DSC) of 0.730 with only 5% of the annotations, outperforming existing methods such as Deep Co-Training (DCT) (0.711) and Mean Teacher (MT) (0.654) and approaching the performance of full supervision (0.891).When the annotation ratio was increased to 10%, the average DSC further improved to 0.801, consistently surpassing all comparative methods.Ablation studies confirmed the effectiveness of each key module.This study provides an efficient and robust solution for automatic segmentation of medical images in low-resource scenarios, offering promising potential for real-world clinical applications.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".