Leveraging healthy population variability in deep learning unsupervised anomaly detection in brain FDG PET

Unsupervised anomaly detection is a popular approach for the analysis of neuroimaging data as it allows identifying a wide variety of anomalies from unlabelled data. It relies on reconstructing a subject-specific model of healthy appearance to which a subject's image can be compared to detect anomalies. In the literature, it is common for anomaly detection to rely on analysing the residual image between the subject's real image and its pseudo-healthy reconstruction. This approach however has limitations partly due to the pseudo-healthy reconstructions being imperfect and to the lack of natural thresholding mechanism. Our proposed method, inspired by Z-scores, leverages the healthy population variability to overcome these limitations. Our experiments conducted on 3D FDG PET scans from the ADNI database demonstrate the effectiveness of our approach in accurately identifying simulated Alzheimer's disease related anomalies.