A Contrastive Learning Approach for Unsupervised Anomaly Detection on Contrast-Enhanced Brain MRI Images

Motivation: Unsupervised anomaly detection (UAD) approaches on T1 contrast-enhanced (T1CE) images are currently not feasible as T1CE images of healthy individuals are not typically available. Goal(s): In this work, we aim to eliminate the need for large labeled datasets that are required for manual anomaly detection on T1CE images. Approach: Using deep learning (DL), we synthesized healthy T1CE images from non-contrast images available in public datasets. We also synthesized healthy-anomalous paired images and forced the DL network to learn the healthy reconstruction. The anomalies were localized by subtracting the reconstruction from the input image. Results: The proposed method achieves state-of-the art dice score coefficients. Impact: This work opens up new avenues of research in unsupervised anomaly detection on T1CE images which has been infeasible due to lack of healthy post-contrast images. We also propose a novel contrastive learning paradigm using synthesis of healthy-anomalous image pairs.