GAN-Based Multiple Adjacent Brain MRI Slice Reconstruction for Unsupervised Alzheimer's Disease Diagnosis

Changhee Han, Leonardo Rundo, Kohei Murao, Zoltán Ádám Milacski, Kazuki Umemoto, Evis Sala, Hideki Nakayama, Shin'ichi Satoh

Abstract: Unsupervised learning can discover various diseases, relying on large-scale unannotated medical images of healthy subjects. Towards this, unsupervised methods reconstruct a single medical image to detect outliers either in the learned feature space or from high reconstruction loss. However, without considering continuity between multiple adjacent slices, they cannot directly discriminate diseases composed of the accumulation of subtle anatomical anomalies, such as Alzheimer’s Disease (AD). Moreover, no study has shown how unsupervised anomaly detection is associated with disease stages. Therefore, we propose a two-step method using Generative Adversarial Network-based multiple adjacent brain MRI slice reconstruction to detect AD at various stages: (Reconstruction) Wasserstein loss with Gradient Penalty $$+$$ $$\ell _1$$ loss—trained on 3 healthy slices to reconstruct the next 3 ones—reconstructs unseen healthy/AD cases; (Diagnosis) Average/Maximum loss (e.g., $$\ell _2$$ loss) per scan discriminates them, comparing the reconstructed/ground truth images. The results show that we can reliably detect AD at a very early stage with Receiver Operating Characteristics-Area Under the Curve (ROC-AUC) 0.780 while also detecting AD at a late stage much more accurately with ROC-AUC 0.917; since our method is fully unsupervised, it should also discover and alert any anomalies including rare disease.