用自编码方法无监督分割口腔放疗坏死3D影像中的异常区域
Latent Anomaly Detection: Masked VQ-GAN for Unsupervised Segmentation in Medical CBCT
- 先训练VQ-GAN重建正常组织，再用掩码恢复学习异常检测特征
- 在真实与模拟数据上实现准确分割，无需标注
- 适合缺乏标注数据的医疗影像分析，可直接用于3D打印
随着治疗技术进步，可定制的3D打印水凝胶创面敷料已可用于颌骨放射性坏死（ONJ）患者。尽管深度学习如nnUNet已能精确分割3D医学图像，但ONJ影像标注数据稀缺，使监督训练难以实施。本研究提出一种无监督训练方法，用于自动识别影像中的异常。我们设计了两阶段训练流程：第一阶段训练VQ-GAN以准确重建正常样本；第二阶段采用随机立方体掩码与ONJ特异性掩码，训练新编码器以恢复被遮挡数据。该方法在模拟和真实患者数据上均实现成功分割，提供快速初始分割方案，显著减轻人工标注负担。此外，结合手工调优后处理后，具有直接用于3D打印的潜力。
原文摘要 · Abstract (English)
Advances in treatment technology now allow for the use of customizable 3D-printed hydrogel wound dressings for patients with osteoradionecrosis (ORN) of the jaw (ONJ). Meanwhile, deep learning has enabled precise segmentation of 3D medical images using tools like nnUNet. However, the scarcity of labeled data in ONJ imaging makes supervised training impractical. This study aims to develop an unsupervised training approach for automatically identifying anomalies in imaging scans. We propose a novel two-stage training pipeline. In the first stage, a VQ-GAN is trained to accurately reconstruct normal subjects. In the second stage, random cube masking and ONJ-specific masking are applied to train a new encoder capable of recovering the data. The proposed method achieves successful segmentation on both simulated and real patient data. This approach provides a fast initial segmentation solution, reducing the burden of manual labeling. Additionally, it has the potential to be directly used for 3D printing when combined with hand-tuned post-processing.
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