Benchmarking Anomaly Detection Methods for Extracardiac Findings in Cardiac MRI

In cardiac magnetic resonance (MR) imaging, an initial set of sequences is acquired to guide the definition of the subsequent cardiac views. These sequences provide a large field of view, enabling the detection of extracardiac findings (ECFs). Although ECFs may have significant clinical relevance, they are typically overlooked since they fall outside the scope of cardiac examinations. The only prior attempt to automatically detect incidental ECFs employed fully supervised methods but faced substantial limitations due to the impracticality of collecting comprehensive samples given the wide range of possible anomalies across various organs. This study investigates the potential of recent anomaly detection (AD) methods to address this challenge. While AD methods have gained popularity, their application has been largely confined to industrial settings or medical imaging tasks such as brain MR or chest X-ray, which exhibit lower anatomical variability and complexity than cardiac MR anatomical sequences. Hereto, twenty state-of-the-art (SOTA) AD methods, including unsupervised, semi-supervised, and open-set supervised learning methodologies, are compared against two fully supervised baselines for detecting ECFs in anatomical planes of cardiac MR. Results from our in-house dataset reveal suboptimal performance of SOTA AD methods, highlighting the need for further research in this domain.