RAIL: Region-Aware Instructive Learning for Semi-Supervised Tooth Segmentation in CBCT

TL;DR

RAIL introduces a dual-group semi-supervised learning framework with region-aware instructive mechanisms to improve 3D tooth segmentation from CBCT scans, especially with limited labeled data.

Contribution

It proposes a novel dual-group dual-student framework with disagreement-focused supervision and confidence-aware learning for semi-supervised tooth segmentation.

Findings

Outperforms state-of-the-art methods on four CBCT datasets.

Effectively handles ambiguous and mislabeled regions.

Enhances pseudo-label reliability and segmentation accuracy.

Abstract

Semi-supervised learning has become a compelling approach for 3D tooth segmentation from CBCT scans, where labeled data is minimal. However, existing methods still face two persistent challenges: limited corrective supervision in structurally ambiguous or mislabeled regions during supervised training and performance degradation caused by unreliable pseudo-labels on unlabeled data. To address these problems, we propose Region-Aware Instructive Learning (RAIL), a dual-group dual-student, semi-supervised framework. Each group contains two student models guided by a shared teacher network. By alternating training between the two groups, RAIL promotes intergroup knowledge transfer and collaborative region-aware instruction while reducing overfitting to the characteristics of any single model. Specifically, RAIL introduces two instructive mechanisms. Disagreement-Focused Supervision (DFS) Controller improves supervised learning by instructing predictions only within areas where student outputs diverge from both ground truth and the best student, thereby concentrating supervision on structurally ambiguous or mislabeled areas. In the unsupervised phase, Confidence-Aware Learning (CAL) Modulator reinforces agreement in regions with high model certainty while reducing the effect of low-confidence predictions during training. This helps prevent our model from learning unstable patterns and improves the overall reliability of pseudo-labels. Extensive experiments on four CBCT tooth segmentation datasets show that RAIL surpasses state-of-the-art methods under limited annotation. Our code will be available at https://github.com/Tournesol-Saturday/RAIL.