Latent Anomaly Detection: Masked VQ-GAN for Unsupervised Segmentation in Medical CBCT
This addresses the scarcity of labeled data in medical imaging for ONJ, offering a fast initial segmentation solution, though it appears incremental as it builds on existing VQ-GAN and masking techniques.
The study tackled the problem of unsupervised anomaly detection in medical CBCT scans for osteoradionecrosis of the jaw, proposing a two-stage masked VQ-GAN method that achieved successful segmentation on simulated and real patient data, reducing manual labeling burden.
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.