RAIL：面向CBCT半监督牙齿分割的区域感知指导学习

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

May 6, 2025

作者: Chuyu Zhao, Hao Huang, Jiashuo Guo, Ziyu Shen, Zhongwei Zhou, Jie Liu, Zekuan Yu

cs.AI

摘要

半监督学习已成为从CBCT扫描中进行三维牙齿分割的一种引人注目的方法，尤其是在标注数据稀缺的情况下。然而，现有方法仍面临两大持续挑战：在监督训练过程中，结构模糊或错误标注区域缺乏有效的纠正监督；以及未标注数据上不可靠的伪标签导致的性能下降。为解决这些问题，我们提出了区域感知指导学习（RAIL），一种双组双学生的半监督框架。每组包含两个由共享教师网络指导的学生模型。通过两组间的交替训练，RAIL促进了组间知识转移和协作式区域感知指导，同时减少了对单一模型特性的过拟合。具体而言，RAIL引入了两种指导机制。分歧聚焦监督（DFS）控制器通过仅在学生输出与真实标签及最佳学生预测存在差异的区域指导预测，从而将监督集中在结构模糊或错误标注的区域，提升了监督学习的效果。在无监督阶段，置信度感知学习（CAL）调节器强化了高模型确定性区域的一致性，同时减少了训练过程中低置信度预测的影响，这有助于防止模型学习不稳定模式，并提高了伪标签的整体可靠性。在四个CBCT牙齿分割数据集上的大量实验表明，RAIL在有限标注条件下超越了现有最先进方法。我们的代码将发布于https://github.com/Tournesol-Saturday/RAIL。

English

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.